---
title: Gemma 4
metatags:
    description: "Deploy Gemma 4 with SGLang - Google's next-generation open models with MoE variants and multimodal support for text, vision, and audio."
tag: NEW
---

import { Gemma4Deployment } from '/src/snippets/autoregressive/gemma4-deployment.jsx';

## 1. Model Introduction

Gemma 4 is Google's next-generation family of open models, building on the Gemma 3 architecture with improved performance, MoE variants, and multimodal support for text, vision, and audio.

**Key Features:**

- **Hybrid Attention**: Combines sliding window and full attention layers for efficient long-context processing
- **Multimodal**: Supports text, image, and audio inputs via dedicated vision and audio encoders
- **MoE Variant**: The 26B-A4B model uses a Mixture-of-Experts architecture for efficient inference
- **Per-Layer Embeddings (PLE)**: Layer-specific token embeddings for enhanced representations
- **Reasoning**: Built-in thinking mode with `gemma4` reasoning parser
- **Tool Calling**: Function call support with streaming via `gemma4` tool call parser
- **Fused Operations**: Triton-optimized RMSNorm + residual + scalar kernels

**Available Models:**

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "33.3%"}} />
    <col style={{width: "33.3%"}} />
    <col style={{width: "33.3%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Architecture</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameters</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Dense</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>~2B</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Dense</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>~4B</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Dense</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>12B</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Dense</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>31B</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>MoE</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>26B total / 4B active</td>
    </tr>
  </tbody>
</table>

## 2. SGLang Installation

Gemma 4 (including the encoder-free unified 12B, [sgl-project/sglang#27167](https://github.com/sgl-project/sglang/pull/27167)) is supported on SGLang main. Install it together with the matching transformers commit:

```bash Command
# Install SGLang from main
pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python'

# Install transformers with Gemma 4 support (encoder-free unified family included)
pip install 'git+https://github.com/huggingface/transformers.git@1423d22f7a3b62e8c70ad67b58ec25cd9b675897'
```

### Docker

`lmsysorg/sglang:latest` (CUDA 13.0, multi-arch `amd64` + `arm64`) runs on both Hopper (H200) and Blackwell (B200 / GB200 / GB300):

```bash Command
docker run --gpus all --ipc=host --shm-size 32g \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 30000:30000 \
  lmsysorg/sglang:latest \
  sglang serve --model-path google/gemma-4-12B-it \
    --reasoning-parser gemma4 --tool-call-parser gemma4 \
    --host 0.0.0.0 --port 30000
```

For other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).

## 3. Model Deployment

### 3.1 Basic Configuration

**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and model variant.

<Gemma4Deployment />

### 3.2 Configuration Tips

- SGLang automatically selects the Triton attention backend for Gemma 4 models (required for bidirectional image-token attention during prefill).
- **Attention backend on Blackwell (B200/sm100)**: SGLang defaults to the `trtllm_mha` backend on sm100, which is fastest for text but applies *causal* attention to image tokens. For multimodal (image) workloads on B200, pass `--attention-backend triton` to restore bidirectional image-token attention and full vision quality. Text-only and audio workloads are unaffected by the default.
- **Gemma 4 26B-A4B on B200**: Use `--mem-fraction-static 0.75` to leave workspace headroom for the Triton MoE path.
- For the 26B-A4B MoE model, consider `--tp 2` for high-throughput workloads.
- **Speculative Decoding (MTP)**: Each Gemma 4 variant ships with a paired `*-assistant` draft model that enables NEXTN multi-token prediction. Enable it via the selector above, or pass `--speculative-algorithm NEXTN --speculative-draft-model-path google/gemma-4-<variant>-it-assistant --speculative-num-steps 5 --speculative-num-draft-tokens 6 --speculative-eagle-topk 1`. MTP can significantly reduce latency for interactive use cases. The 26B-A4B MoE model requires `--tp 2` when MTP is enabled.
- **QAT checkpoints**: Toggle **Checkpoint → QAT** in the selector to target the `qat-q4_0-unquantized` releases. These keep bf16 weights, so memory and TP requirements match the standard checkpoints, and each has a matching `*-qat-q4_0-unquantized-assistant` draft model for MTP.
- Hardware requirements:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "33.3%"}} />
    <col style={{width: "33.3%"}} />
    <col style={{width: "33.3%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Hardware</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>TP</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1x H200 / 1x B200 / 1x B300 / 1x MI300X / 1x MI325X / 1x MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1x H200 / 1x B200 / 1x B300 / 1x MI300X / 1x MI325X / 1x MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1x H200 / 1x B200 / 1x B300</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>2x H200 / 1x B200 / 1x B300 / 1x MI300X / 1x MI325X / 1x MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>2 (H200) / 1 (B200/B300/AMD)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1x H200 / 1x B200 / 1x B300 / 1x MI300X / 1x MI325X / 1x MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
    </tr>
  </tbody>
</table>

### 3.3 AMD GPU Deployment (MI300X / MI325X / MI355X)

SGLang automatically selects the correct attention backend on AMD GPUs. For the small E-models (`gemma-4-E2B-it`, `gemma-4-E4B-it`), disable AITER on AMD GPUs and use the same command line otherwise:

```bash Command
SGLANG_USE_AITER=0 sglang serve --model-path google/gemma-4-E4B-it \
  --reasoning-parser gemma4 \
  --tool-call-parser gemma4 \
  --host 0.0.0.0 --port 30000
```

For `gemma-4-31B-it` and `gemma-4-26B-A4B-it`, the same commands above work on MI300X, MI325X, and MI355X without additional command-line changes.

> **Status**: AMD benchmarks are available in [Section 5.1](#5-1-speed-benchmark).

## 4. Model Invocation

Deploy gemma-4-26B-A4B-it (MoE) with all features enabled:

```bash Command
sglang serve --model-path google/gemma-4-26B-A4B-it \
  --reasoning-parser gemma4 \
  --tool-call-parser gemma4 \
  --host 0.0.0.0 --port 30000
```

#### Speculative Decoding (MTP) Server Commands

Each Gemma 4 variant ships with a paired `*-assistant` draft model for NEXTN multi-token prediction. Use the commands below to enable MTP for the corresponding target model. These match the configuration generated when you toggle **Speculative Decoding (MTP) → Enabled** in the [interactive selector](#3-1-basic-configuration).

```bash Command
# Gemma 4 E2B + MTP
sglang serve \
  --model-path google/gemma-4-E2B-it \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path google/gemma-4-E2B-it-assistant \
  --speculative-num-steps 5 \
  --speculative-num-draft-tokens 6 \
  --speculative-eagle-topk 1 \
  --mem-fraction-static 0.85
```

```bash Command
# Gemma 4 E4B + MTP
sglang serve \
  --model-path google/gemma-4-E4B-it \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path google/gemma-4-E4B-it-assistant \
  --speculative-num-steps 5 \
  --speculative-num-draft-tokens 6 \
  --speculative-eagle-topk 1 \
  --mem-fraction-static 0.85
```

```bash Command
# Gemma 4 12B + MTP (~35% faster single-stream decode on H200)
sglang serve \
  --model-path google/gemma-4-12B-it \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path google/gemma-4-12B-it-assistant \
  --speculative-num-steps 5 \
  --speculative-num-draft-tokens 6 \
  --speculative-eagle-topk 1 \
  --mem-fraction-static 0.85
```

```bash Command
# Gemma 4 31B + MTP
sglang serve \
  --model-path google/gemma-4-31B-it \
  --tp-size 2 \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path google/gemma-4-31B-it-assistant \
  --speculative-num-steps 5 \
  --speculative-num-draft-tokens 6 \
  --speculative-eagle-topk 1 \
  --mem-fraction-static 0.85
```

```bash Command
# Gemma 4 26B-A4B + MTP
sglang serve \
  --model-path google/gemma-4-26B-A4B-it \
  --tp-size 2 \
  --speculative-algorithm NEXTN \
  --speculative-draft-model-path google/gemma-4-26B-A4B-it-assistant \
  --speculative-num-steps 5 \
  --speculative-num-draft-tokens 6 \
  --speculative-eagle-topk 1 \
  --mem-fraction-static 0.85
```

### 4.1 Basic Usage

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

response = client.chat.completions.create(
    model="google/gemma-4-26B-A4B-it",
    messages=[
        {"role": "user", "content": "What are the key differences between TCP and UDP?"}
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)
```

<details>
<summary>Example Output</summary>

```text Output
The fundamental difference between **TCP (Transmission Control Protocol)** and **UDP (User Datagram
Protocol)** lies in how they prioritize data integrity versus speed.

### 1. Connection Type
*   **TCP (Connection-Oriented):** Before any data is sent, TCP performs a "three-way handshake."
    The sender and receiver exchange signals to establish a formal connection.
*   **UDP (Connectionless):** UDP does not establish a connection. It simply starts blasting packets
    to the destination IP address without checking if the receiver is ready.

### 2. Reliability and Error Checking
*   **TCP (Reliable):** If a packet is lost or arrives corrupted, TCP detects the error and
    retransmits the missing data.
*   **UDP (Unreliable):** If a packet is lost or corrupted, it is simply discarded. There is no
    mechanism to ask for a retransmission.

### 3. Ordering of Data
*   **TCP (Ordered):** Segments are assigned sequence numbers and reassembled in the correct order.
*   **UDP (Unordered):** Packets may arrive in a different order than sent.

### 4. Speed and Overhead
*   **TCP (Slower):** Managing connections, tracking, and retransmissions adds significant overhead.
*   **UDP (Faster):** No handshake, no tracking — extremely fast and ideal for real-time needs.

| Feature | TCP | UDP |
| :--- | :--- | :--- |
| **Connection** | Connection-oriented | Connectionless |
| **Reliability** | Guaranteed delivery | Best-effort |
| **Ordering** | Maintains strict order | No guaranteed order |
| **Speed** | Slower (High overhead) | Faster (Low overhead) |
```

</details>

### 4.2 Vision Input

Gemma 4 multimodal variants accept images alongside text:

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

response = client.chat.completions.create(
    model="google/gemma-4-26B-A4B-it",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://farm4.staticflickr.com/3175/2653711032_804ff86d81_z.jpg"
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this image in detail."
                }
            ]
        }
    ],
    max_tokens=1024
)

print(response.choices[0].message.content)
```

<details>
<summary>Example Output</summary>

```text Output
A vertical, full shot shows a girl and a boy standing in front of a giant teddy bear. The boy, who
is on the left, is of South Asian descent, has short dark hair, and is smiling at the camera. He is
wearing a navy blue sweatshirt with a white collar, blue jeans, and white, black, and red sneakers.
The girl, on the right, is also of South Asian descent and has long, dark hair. She is smiling at
the camera and is wearing a pink t-shirt, a white long-sleeve shirt underneath, blue jeans, and pink
sneakers. The giant teddy bear is light brown and is standing behind the two children. The bear has
large, dark eyes and a black nose. In the background, on the left, there is a large wooden basket
filled with small teddy bears. To the left of the basket, an American flag is hanging on the wall.
On the right side of the image, there is a green leafy plant. The floor is a dark purple carpet. The
lighting is bright and even.
```

</details>

### 4.3 Reasoning (Thinking Mode)

Gemma 4 supports hybrid reasoning. Thinking is **not enabled by default** — pass `chat_template_kwargs: {"enable_thinking": true}` via `extra_body` to activate it. The reasoning parser separates thinking and content, returning the thinking process via `reasoning_content` in the streaming response.

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

response = client.chat.completions.create(
    model="google/gemma-4-26B-A4B-it",
    messages=[
        {"role": "user", "content": "Solve step by step: If a train travels at 60 km/h for 2.5 hours, how far does it go?"}
    ],
    max_tokens=4096,
    stream=True,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}}
)

thinking_started = False
has_thinking = False
has_answer = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()
```

<details>
<summary>Example Output</summary>

```text Output
=============== Thinking =================
*   Input: Speed = 60 km/h, Time = 2.5 hours.
    *   Goal: Find the distance traveled.
    *   Distance = Speed × Time.
    *   Step 1: Identify given values. Speed = 60 km/h, Time = 2.5 hours
    *   Step 2: Formula. Distance = Speed × Time
    *   Step 3: Calculation. 60 × 2.5
        Mental math: 60 × 2 = 120; 60 × 0.5 = 30; 120 + 30 = 150.
    *   Step 4: Final Result. 150 km.

=============== Content =================
To find the distance traveled, you can follow these steps:

### 1. Identify the given information:
*   **Speed:** 60 km/h
*   **Time:** 2.5 hours

### 2. Use the distance formula:
Distance = Speed × Time

### 3. Substitute the values:
Distance = 60 km/h × 2.5 hours

### 4. Perform the calculation:
*   60 × 2 = 120
*   60 × 0.5 = 30
*   120 + 30 = 150

**Final Answer: The train travels 150 km.**
```

</details>

### 4.4 Tool Calling

Gemma 4 supports function calling with the `gemma4` tool call parser. Enable it during deployment with `--tool-call-parser gemma4`.

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="google/gemma-4-26B-A4B-it",
    messages=[
        {"role": "user", "content": "What's the weather in Tokyo?"}
    ],
    tools=tools,
    stream=True
)

thinking_started = False
has_thinking = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            if has_thinking and thinking_started:
                print("\n=============== Tool Calls ================", flush=True)
                thinking_started = False
            for tool_call in delta.tool_calls:
                if tool_call.function:
                    print(f"Tool Call: {tool_call.function.name}")
                    print(f"   Arguments: {tool_call.function.arguments}")

        if delta.content:
            print(delta.content, end="", flush=True)

print()
```

<details>
<summary>Example Output</summary>

```text Output
=============== Tool Calls ================
Tool Call: get_weather
   Arguments: {"location": "Tokyo"}
```

</details>

### 4.5 Audio Input

The audio-capable Gemma 4 variants (`gemma-4-E2B-it`, `gemma-4-E4B-it`, `gemma-4-12B-it`) accept raw audio alongside text. Pass the waveform as a base64 `audio_url` data URI (16 kHz mono WAV works well):

```python Example
import base64
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

with open("sample.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="google/gemma-4-12B-it",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "audio_url", "audio_url": {"url": f"data:audio/wav;base64,{audio_b64}"}},
                {"type": "text", "text": "Transcribe the speech in this audio exactly."},
            ],
        }
    ],
    max_tokens=256,
    temperature=0,
)

print(response.choices[0].message.content)
```

<details>
<summary>Example Output</summary>

```text Output
Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
```

</details>

For best ASR quality, use the recommended transcription prompt structure:

```text Prompt
Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.

Follow these specific instructions for formatting the answer:
* Only output the transcription, with no newlines.
* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
```

For speech translation (AST), ask for the transcription in the source language first, then the translation: *"Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}. ..."*

## 5. Benchmark

### 5.1 Speed Benchmark

**Test Environment:**

- Hardware: H200
- SGLang Version: gemma4 branch

#### gemma-4-E2B-it (1x H200, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-E2B-it
```

**Latency Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  17.44
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.57
Output token throughput (tok/s):         242.03
Total token throughput (tok/s):          591.94
Mean TTFT (ms):                          50.19
Median TTFT (ms):                        54.22
Mean TPOT (ms):                          3.99
Median ITL (ms):                         4.05
==================================================
```

**Latency Benchmark (Image)**

```bash Command
python3 -m sglang.bench_serving --backend sglang-oai-chat \
  --host 0.0.0.0 --port 30000 \
  --dataset-name image --image-count 2 --image-resolution 720p \
  --random-input-len 128 --random-output-len 1024 \
  --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  18.05
Total input tokens:                      6097
Total input vision tokens:               5340
Total generated tokens:                  4220
Request throughput (req/s):              0.55
Output token throughput (tok/s):         233.84
Total token throughput (tok/s):          571.69
Mean TTFT (ms):                          109.59
Median TTFT (ms):                        112.62
Mean TPOT (ms):                          4.01
Median ITL (ms):                         4.04
==================================================
```

**Throughput Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 1000 --max-concurrency 100
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  51.73
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              19.33
Output token throughput (tok/s):         9876.36
Peak output token throughput (tok/s):    13863.00
Total token throughput (tok/s):          19791.14
Mean TTFT (ms):                          86.57
Mean TPOT (ms):                          9.56
Median ITL (ms):                         5.99
==================================================
```

**Throughput Benchmark (Image)**

```bash Command
python3 -m sglang.bench_serving --backend sglang-oai-chat \
  --host 0.0.0.0 --port 30000 \
  --dataset-name image --image-count 2 --image-resolution 720p \
  --random-input-len 128 --random-output-len 1024 \
  --num-prompts 1000 --max-concurrency 100
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  89.07
Total input tokens:                      617799
Total input vision tokens:               534000
Total generated tokens:                  510855
Request throughput (req/s):              11.23
Output token throughput (tok/s):         5735.75
Peak output token throughput (tok/s):    12823.00
Total token throughput (tok/s):          12672.23
Mean TTFT (ms):                          636.46
Mean TPOT (ms):                          16.34
Median ITL (ms):                         5.68
==================================================
```

#### gemma-4-E4B-it (1x H200, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-E4B-it
```

**Latency Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  24.49
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.41
Output token throughput (tok/s):         172.32
Total token throughput (tok/s):          421.45
Mean TTFT (ms):                          52.76
Median TTFT (ms):                        53.66
Mean TPOT (ms):                          5.64
Median ITL (ms):                         5.74
==================================================
```

**Latency Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  25.04
Total input tokens:                      6124
Total input vision tokens:               5340
Total generated tokens:                  4220
Request throughput (req/s):              0.40
Output token throughput (tok/s):         168.54
Total token throughput (tok/s):          413.13
Mean TTFT (ms):                          110.15
Median TTFT (ms):                        108.24
Mean TPOT (ms):                          5.66
Median ITL (ms):                         5.73
==================================================
```

**Throughput Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  72.95
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              13.71
Output token throughput (tok/s):         7002.68
Peak output token throughput (tok/s):    9878.00
Total token throughput (tok/s):          14032.60
Mean TTFT (ms):                          166.33
Mean TPOT (ms):                          13.36
Median ITL (ms):                         8.88
==================================================
```

**Throughput Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  108.99
Total input tokens:                      616952
Total input vision tokens:               534000
Total generated tokens:                  510855
Request throughput (req/s):              9.18
Output token throughput (tok/s):         4687.38
Peak output token throughput (tok/s):    9277.00
Total token throughput (tok/s):          10348.25
Mean TTFT (ms):                          626.17
Mean TPOT (ms):                          20.00
Median ITL (ms):                         8.64
==================================================
```

#### gemma-4-31B-it (2x H200, TP=2)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-31B-it --tp 2
```

**Latency Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  53.05
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.19
Output token throughput (tok/s):         79.55
Total token throughput (tok/s):          194.55
Mean TTFT (ms):                          72.77
Median TTFT (ms):                        75.05
Mean TPOT (ms):                          12.32
Median ITL (ms):                         12.53
==================================================
```

**Latency Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  53.78
Total input tokens:                      6162
Total input vision tokens:               5340
Total generated tokens:                  4220
Request throughput (req/s):              0.19
Output token throughput (tok/s):         78.46
Total token throughput (tok/s):          193.03
Mean TTFT (ms):                          143.35
Median TTFT (ms):                        146.85
Mean TPOT (ms):                          12.37
Median ITL (ms):                         12.48
==================================================
```

**Throughput Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  182.00
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              5.49
Output token throughput (tok/s):         2806.82
Peak output token throughput (tok/s):    3798.00
Total token throughput (tok/s):          5624.56
Mean TTFT (ms):                          324.67
Mean TPOT (ms):                          33.95
Median ITL (ms):                         25.44
==================================================
```

**Throughput Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  236.46
Total input tokens:                      621630
Total input vision tokens:               534000
Total generated tokens:                  510855
Request throughput (req/s):              4.23
Output token throughput (tok/s):         2160.42
Peak output token throughput (tok/s):    3745.00
Total token throughput (tok/s):          4789.30
Mean TTFT (ms):                          952.02
Mean TPOT (ms):                          44.17
Median ITL (ms):                         26.81
==================================================
```

#### gemma-4-26B-A4B-it (MoE, 1x H200, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-26B-A4B-it
```

> **Tip**: Consider `--tp 2` for high-throughput workloads.

**Latency Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  25.00
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.40
Output token throughput (tok/s):         168.81
Total token throughput (tok/s):          412.85
Mean TTFT (ms):                          103.74
Median TTFT (ms):                        46.57
Mean TPOT (ms):                          5.60
Median ITL (ms):                         5.78
==================================================
```

**Latency Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  25.31
Total input tokens:                      6164
Total input vision tokens:               5340
Total generated tokens:                  4220
Request throughput (req/s):              0.40
Output token throughput (tok/s):         166.70
Total token throughput (tok/s):          410.20
Mean TTFT (ms):                          129.22
Median TTFT (ms):                        132.54
Mean TPOT (ms):                          5.68
Median ITL (ms):                         5.75
==================================================
```

**Throughput Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  138.98
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              7.20
Output token throughput (tok/s):         3675.81
Peak output token throughput (tok/s):    4799.00
Total token throughput (tok/s):          7365.91
Mean TTFT (ms):                          153.77
Mean TPOT (ms):                          25.95
Median ITL (ms):                         20.23
==================================================
```

**Throughput Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  186.38
Total input tokens:                      621146
Total input vision tokens:               534000
Total generated tokens:                  510855
Request throughput (req/s):              5.37
Output token throughput (tok/s):         2740.86
Peak output token throughput (tok/s):    4962.00
Total token throughput (tok/s):          6073.47
Mean TTFT (ms):                          854.71
Mean TPOT (ms):                          34.64
Median ITL (ms):                         19.08
==================================================
```

#### gemma-4-31B-it (1x MI300X, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-31B-it
```

> **Note**: The 31B dense model fits on a single MI300X (192 GB VRAM) at TP=1, unlike H200 (141 GB) which requires TP=2.

**Latency Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  103.55
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.10
Output token throughput (tok/s):         40.75
Total token throughput (tok/s):          99.67
Mean TTFT (ms):                          152.35
Median TTFT (ms):                        169.66
Mean TPOT (ms):                          24.13
Median ITL (ms):                         24.23
==================================================
```

**Throughput Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 1000 --max-concurrency 100
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  441.59
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              2.26
Output token throughput (tok/s):         1156.85
Peak output token throughput (tok/s):    1759.00
Total token throughput (tok/s):          2318.19
Mean TTFT (ms):                          819.22
Mean TPOT (ms):                          82.51
Median ITL (ms):                         63.45
==================================================
```

#### gemma-4-26B-A4B-it (MoE, 1x MI300X, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-26B-A4B-it
```

**Latency Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  43.73
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.23
Output token throughput (tok/s):         96.49
Total token throughput (tok/s):          236.00
Mean TTFT (ms):                          185.58
Median TTFT (ms):                        90.18
Mean TPOT (ms):                          9.78
Median ITL (ms):                         9.57
==================================================
```

**Throughput Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 1000 --max-concurrency 100
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  219.43
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              4.56
Output token throughput (tok/s):         2328.05
Peak output token throughput (tok/s):    3500.00
Total token throughput (tok/s):          4665.16
Mean TTFT (ms):                          168.44
Mean TPOT (ms):                          41.23
Median ITL (ms):                         29.31
==================================================
```

#### gemma-4-12B-it (1x H200, TP=1)

Server Launch Command:
```bash Command
sglang serve --model-path google/gemma-4-12B-it
```

**Latency Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  38.66
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.26
Output token throughput (tok/s):         109.15
Total token throughput (tok/s):          266.94
Mean TTFT (ms):                          33.08
Median TTFT (ms):                        33.71
Mean TPOT (ms):                          9.02
Median ITL (ms):                         9.19
==================================================
```

**Latency Benchmark (Image)**

```bash Command
python3 -m sglang.bench_serving --backend sglang-oai-chat \
  --host 0.0.0.0 --port 30000 \
  --dataset-name image --image-count 2 --image-resolution 720p \
  --random-input-len 128 --random-output-len 1024 \
  --num-prompts 10 --max-concurrency 1
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  39.36
Total input vision tokens:               5320
Total generated tokens:                  4220
Request throughput (req/s):              0.25
Output token throughput (tok/s):         107.23
Total token throughput (tok/s):          263.62
Mean TTFT (ms):                          94.98
Median TTFT (ms):                        97.33
Mean TPOT (ms):                          9.08
Median ITL (ms):                         9.17
==================================================
```

**Throughput Benchmark (Text)**

```bash Command
python3 -m sglang.bench_serving --backend sglang \
  --host 0.0.0.0 --port 30000 \
  --dataset-name random --num-prompts 1000 --max-concurrency 100
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  130.44
Total input tokens:                      512842
Total generated tokens:                  510855
Request throughput (req/s):              7.67
Output token throughput (tok/s):         3916.46
Total token throughput (tok/s):          7848.15
Mean TTFT (ms):                          207.49
Median TTFT (ms):                        76.95
Mean TPOT (ms):                          24.38
Median ITL (ms):                         17.89
==================================================
```

**Throughput Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  147.57
Total input tokens:                      619609
Total input vision tokens:               532000
Total generated tokens:                  510855
Request throughput (req/s):              6.78
Output token throughput (tok/s):         3461.79
Total token throughput (tok/s):          7660.54
Mean TTFT (ms):                          438.40
Median TTFT (ms):                        129.83
Mean TPOT (ms):                          27.12
Median ITL (ms):                         19.16
==================================================
```

#### gemma-4-12B-it (1x B200, TP=1)

Server Launch Command:
```bash Command
# Text/audio: the sm100 default (trtllm_mha) is fastest.
# For image workloads add --attention-backend triton (bidirectional image attention).
sglang serve --model-path google/gemma-4-12B-it --attention-backend triton
```

**Latency Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  30.46
Output token throughput (tok/s):         138.55
Total token throughput (tok/s):          338.85
Mean TTFT (ms):                          28.14
Median TTFT (ms):                        29.74
Mean TPOT (ms):                          7.08
Median ITL (ms):                         7.26
==================================================
```

**Latency Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  31.43
Total input vision tokens:               5320
Total generated tokens:                  4220
Request throughput (req/s):              0.32
Output token throughput (tok/s):         134.26
Total token throughput (tok/s):          329.57
Mean TTFT (ms):                          115.51
Median TTFT (ms):                        74.27
Mean TPOT (ms):                          7.14
Median ITL (ms):                         7.24
==================================================
```

**Throughput Benchmark (Text)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  92.94
Request throughput (req/s):              10.76
Output token throughput (tok/s):         5496.55
Total token throughput (tok/s):          11014.49
Mean TTFT (ms):                          120.89
Median TTFT (ms):                        45.00
Mean TPOT (ms):                          17.23
Median ITL (ms):                         14.30
==================================================
```

**Throughput Benchmark (Image)**

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Max request concurrency:                 100
Successful requests:                     998
Benchmark duration (s):                  107.82
Total input tokens:                      617971
Total input vision tokens:               530936
Total generated tokens:                  508951
Request throughput (req/s):              9.26
Output token throughput (tok/s):         4720.29
Total token throughput (tok/s):          10451.68
Mean TTFT (ms):                          425.89
Median TTFT (ms):                        109.57
Mean TPOT (ms):                          19.45
Median ITL (ms):                         15.11
==================================================
```

> **Performance tuning**: On B200, raising `--scheduler-recv-interval` to 16 lifted text throughput from 5497 to 5673 tok/s output (≈ +3%) at concurrency 100 with no accuracy change, by reducing the scheduler's per-step Python overhead. It is a safe, low-risk knob for high-concurrency serving.

### 5.2 Accuracy Benchmark

**Test Environment:**

- Hardware: H200
- SGLang Version: gemma4 branch

#### MMLU

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "16.7%"}} />
    <col style={{width: "16.7%"}} />
    <col style={{width: "16.7%"}} />
    <col style={{width: "16.7%"}} />
    <col style={{width: "16.7%"}} />
    <col style={{width: "16.7%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Humanities</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Social Sciences</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>STEM</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Other</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Overall</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.621</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.739</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.830</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.736</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.720**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.703</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.862</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.902</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.825</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.810**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.784</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.888</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.946</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.861</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.859**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.878</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.921</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.884</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.911</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.896**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.853</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.906</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.938</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.886</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.891**</td>
    </tr>
  </tbody>
</table>

#### GSM8K

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "20.0%"}} />
    <col style={{width: "20.0%"}} />
    <col style={{width: "20.0%"}} />
    <col style={{width: "20.0%"}} />
    <col style={{width: "20.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Accuracy</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Invalid</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Latency (s)</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Output Throughput (tok/s)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.170</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.000</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>3.990</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>8041.739</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.745</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.000</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>4.174</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4672.030</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.431</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.052</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>55.105</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>6580.229</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.805</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.005</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>16.148</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1559.914</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.450</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.010</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>13.001</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4089.457</td>
    </tr>
  </tbody>
</table>

> **Note**: These GSM8K numbers use the raw few-shot completion harness (`sglang.test.few_shot_gsm8k`). `gemma-4-12B-it` is reasoning-oriented and is under-elicited by raw few-shot prompting; with the chat template it scores **0.950** on the same 1319 GSM8K test questions (`sglang.test.run_eval --eval-name gsm8k`).

#### gemma-4-12B-it with sgl-eval

`gemma-4-12B-it` is reasoning-oriented and answers verbosely (step-by-step) rather than emitting a terse final line. Strict last-line `Answer: $LETTER` extraction (as in `sglang.test.run_eval`) therefore undercounts its correct answers. [sgl-eval](https://github.com/sgl-project/sgl-eval) — sgl-project's evaluation CLI, which uses robust answer extraction — gives a faithful score on the served model:

| Benchmark | Examples | Accuracy |
| --- | --- | --- |
| MMLU | 2000 | **0.878** |
| GSM8K | 1319 | **0.960** |

Reproduce against a running server (`--base-url` points at your endpoint):

```bash Command
pip install git+https://github.com/sgl-project/sgl-eval

# Sanity-check the endpoint
sgl-eval ping --base-url http://localhost:30000/v1

# Run the benchmarks (greedy, single-shot)
sgl-eval run gsm8k --base-url http://localhost:30000/v1
sgl-eval run mmlu  --base-url http://localhost:30000/v1 --num-examples 2000
```

#### MMMU

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "50.0%"}} />
    <col style={{width: "50.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Overall</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.307**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.396**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.683**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.589**</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>**0.549**</td>
    </tr>
  </tbody>
</table>

<details>
<summary>MMMU detailed scores (per domain)</summary>

**gemma-4-E2B-it**

```json Config
{"Overall-Art and Design": {"num": 120, "acc": 0.45}, "Art": {"num": 30, "acc": 0.5}, "Art_Theory": {"num": 30, "acc": 0.467}, "Design": {"num": 30, "acc": 0.5}, "Music": {"num": 30, "acc": 0.333}, "Overall-Business": {"num": 150, "acc": 0.26}, "Accounting": {"num": 30, "acc": 0.367}, "Economics": {"num": 30, "acc": 0.233}, "Finance": {"num": 30, "acc": 0.2}, "Manage": {"num": 30, "acc": 0.233}, "Marketing": {"num": 30, "acc": 0.267}, "Overall-Science": {"num": 150, "acc": 0.273}, "Biology": {"num": 30, "acc": 0.233}, "Chemistry": {"num": 30, "acc": 0.267}, "Geography": {"num": 30, "acc": 0.367}, "Math": {"num": 30, "acc": 0.233}, "Physics": {"num": 30, "acc": 0.267}, "Overall-Health and Medicine": {"num": 150, "acc": 0.273}, "Basic_Medical_Science": {"num": 30, "acc": 0.5}, "Clinical_Medicine": {"num": 30, "acc": 0.233}, "Diagnostics_and_Laboratory_Medicine": {"num": 30, "acc": 0.233}, "Pharmacy": {"num": 30, "acc": 0.3}, "Public_Health": {"num": 30, "acc": 0.1}, "Overall-Humanities and Social Science": {"num": 120, "acc": 0.4}, "History": {"num": 30, "acc": 0.4}, "Literature": {"num": 30, "acc": 0.567}, "Sociology": {"num": 30, "acc": 0.333}, "Psychology": {"num": 30, "acc": 0.3}, "Overall-Tech and Engineering": {"num": 210, "acc": 0.252}, "Agriculture": {"num": 30, "acc": 0.333}, "Architecture_and_Engineering": {"num": 30, "acc": 0.267}, "Computer_Science": {"num": 30, "acc": 0.233}, "Electronics": {"num": 30, "acc": 0.1}, "Energy_and_Power": {"num": 30, "acc": 0.3}, "Materials": {"num": 30, "acc": 0.2}, "Mechanical_Engineering": {"num": 30, "acc": 0.333}, "Overall": {"num": 900, "acc": 0.307}}
```

**gemma-4-E4B-it**

```json Config
{"Overall-Art and Design": {"num": 120, "acc": 0.458}, "Art": {"num": 30, "acc": 0.433}, "Art_Theory": {"num": 30, "acc": 0.567}, "Design": {"num": 30, "acc": 0.667}, "Music": {"num": 30, "acc": 0.167}, "Overall-Business": {"num": 150, "acc": 0.287}, "Accounting": {"num": 30, "acc": 0.233}, "Economics": {"num": 30, "acc": 0.467}, "Finance": {"num": 30, "acc": 0.133}, "Manage": {"num": 30, "acc": 0.3}, "Marketing": {"num": 30, "acc": 0.3}, "Overall-Science": {"num": 150, "acc": 0.28}, "Biology": {"num": 30, "acc": 0.333}, "Chemistry": {"num": 30, "acc": 0.133}, "Geography": {"num": 30, "acc": 0.4}, "Math": {"num": 30, "acc": 0.2}, "Physics": {"num": 30, "acc": 0.333}, "Overall-Health and Medicine": {"num": 150, "acc": 0.427}, "Basic_Medical_Science": {"num": 30, "acc": 0.4}, "Clinical_Medicine": {"num": 30, "acc": 0.533}, "Diagnostics_and_Laboratory_Medicine": {"num": 30, "acc": 0.4}, "Pharmacy": {"num": 30, "acc": 0.4}, "Public_Health": {"num": 30, "acc": 0.4}, "Overall-Humanities and Social Science": {"num": 120, "acc": 0.7}, "History": {"num": 30, "acc": 0.633}, "Literature": {"num": 30, "acc": 0.867}, "Sociology": {"num": 30, "acc": 0.733}, "Psychology": {"num": 30, "acc": 0.567}, "Overall-Tech and Engineering": {"num": 210, "acc": 0.324}, "Agriculture": {"num": 30, "acc": 0.533}, "Architecture_and_Engineering": {"num": 30, "acc": 0.3}, "Computer_Science": {"num": 30, "acc": 0.367}, "Electronics": {"num": 30, "acc": 0.133}, "Energy_and_Power": {"num": 30, "acc": 0.4}, "Materials": {"num": 30, "acc": 0.2}, "Mechanical_Engineering": {"num": 30, "acc": 0.333}, "Overall": {"num": 900, "acc": 0.396}}
```

**gemma-4-12B-it**

```json Config
{"Overall-Art and Design": {"num": 120, "acc": 0.667}, "Art": {"num": 30, "acc": 0.7}, "Art_Theory": {"num": 30, "acc": 0.867}, "Design": {"num": 30, "acc": 0.767}, "Music": {"num": 30, "acc": 0.333}, "Overall-Business": {"num": 150, "acc": 0.747}, "Accounting": {"num": 30, "acc": 0.767}, "Economics": {"num": 30, "acc": 0.767}, "Finance": {"num": 30, "acc": 0.633}, "Manage": {"num": 30, "acc": 0.7}, "Marketing": {"num": 30, "acc": 0.867}, "Overall-Science": {"num": 150, "acc": 0.647}, "Biology": {"num": 30, "acc": 0.6}, "Chemistry": {"num": 30, "acc": 0.633}, "Geography": {"num": 30, "acc": 0.567}, "Math": {"num": 30, "acc": 0.6}, "Physics": {"num": 30, "acc": 0.833}, "Overall-Health and Medicine": {"num": 150, "acc": 0.68}, "Basic_Medical_Science": {"num": 30, "acc": 0.667}, "Clinical_Medicine": {"num": 30, "acc": 0.633}, "Diagnostics_and_Laboratory_Medicine": {"num": 30, "acc": 0.267}, "Pharmacy": {"num": 30, "acc": 0.833}, "Public_Health": {"num": 30, "acc": 1.0}, "Overall-Humanities and Social Science": {"num": 120, "acc": 0.817}, "History": {"num": 30, "acc": 0.8}, "Literature": {"num": 30, "acc": 0.9}, "Sociology": {"num": 30, "acc": 0.8}, "Psychology": {"num": 30, "acc": 0.767}, "Overall-Tech and Engineering": {"num": 210, "acc": 0.6}, "Agriculture": {"num": 30, "acc": 0.467}, "Architecture_and_Engineering": {"num": 30, "acc": 0.667}, "Computer_Science": {"num": 30, "acc": 0.733}, "Electronics": {"num": 30, "acc": 0.567}, "Energy_and_Power": {"num": 30, "acc": 0.667}, "Materials": {"num": 30, "acc": 0.567}, "Mechanical_Engineering": {"num": 30, "acc": 0.533}, "Overall": {"num": 900, "acc": 0.683}}
```

**gemma-4-31B-it**

```json Config
{"Overall-Art and Design": {"num": 120, "acc": 0.667}, "Art": {"num": 30, "acc": 0.667}, "Art_Theory": {"num": 30, "acc": 0.867}, "Design": {"num": 30, "acc": 0.8}, "Music": {"num": 30, "acc": 0.333}, "Overall-Business": {"num": 150, "acc": 0.573}, "Accounting": {"num": 30, "acc": 0.633}, "Economics": {"num": 30, "acc": 0.733}, "Finance": {"num": 30, "acc": 0.433}, "Manage": {"num": 30, "acc": 0.533}, "Marketing": {"num": 30, "acc": 0.533}, "Overall-Science": {"num": 150, "acc": 0.527}, "Biology": {"num": 30, "acc": 0.667}, "Chemistry": {"num": 30, "acc": 0.567}, "Geography": {"num": 30, "acc": 0.5}, "Math": {"num": 30, "acc": 0.267}, "Physics": {"num": 30, "acc": 0.633}, "Overall-Health and Medicine": {"num": 150, "acc": 0.673}, "Basic_Medical_Science": {"num": 30, "acc": 0.733}, "Clinical_Medicine": {"num": 30, "acc": 0.533}, "Diagnostics_and_Laboratory_Medicine": {"num": 30, "acc": 0.467}, "Pharmacy": {"num": 30, "acc": 0.8}, "Public_Health": {"num": 30, "acc": 0.833}, "Overall-Humanities and Social Science": {"num": 120, "acc": 0.825}, "History": {"num": 30, "acc": 0.833}, "Literature": {"num": 30, "acc": 0.867}, "Sociology": {"num": 30, "acc": 0.767}, "Psychology": {"num": 30, "acc": 0.833}, "Overall-Tech and Engineering": {"num": 210, "acc": 0.405}, "Agriculture": {"num": 30, "acc": 0.667}, "Architecture_and_Engineering": {"num": 30, "acc": 0.2}, "Computer_Science": {"num": 30, "acc": 0.567}, "Electronics": {"num": 30, "acc": 0.333}, "Energy_and_Power": {"num": 30, "acc": 0.533}, "Materials": {"num": 30, "acc": 0.3}, "Mechanical_Engineering": {"num": 30, "acc": 0.233}, "Overall": {"num": 900, "acc": 0.589}}
```

**gemma-4-26B-A4B-it**

```json Config
{"Overall-Art and Design": {"num": 120, "acc": 0.717}, "Art": {"num": 30, "acc": 0.733}, "Art_Theory": {"num": 30, "acc": 0.833}, "Design": {"num": 30, "acc": 0.867}, "Music": {"num": 30, "acc": 0.433}, "Overall-Business": {"num": 150, "acc": 0.493}, "Accounting": {"num": 30, "acc": 0.533}, "Economics": {"num": 30, "acc": 0.533}, "Finance": {"num": 30, "acc": 0.333}, "Manage": {"num": 30, "acc": 0.5}, "Marketing": {"num": 30, "acc": 0.567}, "Overall-Science": {"num": 150, "acc": 0.473}, "Biology": {"num": 30, "acc": 0.633}, "Chemistry": {"num": 30, "acc": 0.367}, "Geography": {"num": 30, "acc": 0.533}, "Math": {"num": 30, "acc": 0.267}, "Physics": {"num": 30, "acc": 0.567}, "Overall-Health and Medicine": {"num": 150, "acc": 0.62}, "Basic_Medical_Science": {"num": 30, "acc": 0.767}, "Clinical_Medicine": {"num": 30, "acc": 0.533}, "Diagnostics_and_Laboratory_Medicine": {"num": 30, "acc": 0.433}, "Pharmacy": {"num": 30, "acc": 0.7}, "Public_Health": {"num": 30, "acc": 0.667}, "Overall-Humanities and Social Science": {"num": 120, "acc": 0.758}, "History": {"num": 30, "acc": 0.8}, "Literature": {"num": 30, "acc": 0.833}, "Sociology": {"num": 30, "acc": 0.733}, "Psychology": {"num": 30, "acc": 0.667}, "Overall-Tech and Engineering": {"num": 210, "acc": 0.376}, "Agriculture": {"num": 30, "acc": 0.633}, "Architecture_and_Engineering": {"num": 30, "acc": 0.367}, "Computer_Science": {"num": 30, "acc": 0.533}, "Electronics": {"num": 30, "acc": 0.167}, "Energy_and_Power": {"num": 30, "acc": 0.367}, "Materials": {"num": 30, "acc": 0.367}, "Mechanical_Engineering": {"num": 30, "acc": 0.2}, "Overall": {"num": 900, "acc": 0.549}}
```

#### ASR

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>WER</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Avg Latency (s)</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Throughput (req/s)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>23.86%</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.212</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>2.99</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>29.55%</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.366</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>2.46</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Supported (see §4.5)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Not Supported</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Not Supported</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
  </tbody>
</table>

#### FLEUR (EN_US)

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>WER</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Avg Latency (s)</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Throughput (req/s)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E2B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>7.37%</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.8963s</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>16.25</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-E4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>6.08%</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.8707s</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>16.20</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-12B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Supported (see §4.5)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-31B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Not Supported</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>gemma-4-26B-A4B-it</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Not Supported</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
  </tbody>
</table>

### 5.3 Logits correctness validation

**gemma-4-E2B-it**
```shell Command
$ python -m sglang.bench_one_batch --correct --model google/gemma-4-E2B-it ....
prefill logits (final): tensor([[-25.3063,  -2.5718, -10.3674,  ..., -25.3779, -25.5181, -25.2337]],
       device='cuda:0')
....

$ python scripts/playground/reference_hf.py --model-path google/gemma-4-E2B-it
....
prefill logits (final) tensor([-25.3281,  -2.1367, -10.2266,  ..., -25.4375, -25.5000, -25.2500],
       device='cuda:0', dtype=torch.float16)
....
```

**gemma-4-E4B-it**

```shell Command
$ python -m sglang.bench_one_batch --correct --model google/gemma-4-E4B-it ....
prefill logits (final): tensor([[-17.6478,   7.9901,  -5.6505,  ..., -17.5658, -17.6478, -17.7293]],
       device='cuda:0')
....

$ python scripts/playground/reference_hf.py --model-path google/gemma-4-E4B-it
....
prefill logits (final) tensor([-17.5625,   8.0469,  -5.5742,  ..., -17.4688, -17.5625, -17.6719],
       device='cuda:0', dtype=torch.float16)
....
```

**gemma-4-31B-it**
```shell Command
$ python -m sglang.bench_one_batch --correct --model google/gemma-4-31B-it ....
prefill logits (final): tensor([[-2.0748,  1.1245, -7.4356,  ..., -2.1059, -2.1525, -2.2303]],
       device='cuda:0')
....

$ python scripts/playground/reference_hf.py --model-path google/gemma-4-31B-it
....
prefill logits (final) tensor([-2.1133,  1.2656, -7.4766,  ..., -2.1523, -2.2012, -2.2695],
       device='cuda:0', dtype=torch.float16)
....
```

</details>
