---
title: Laguna-S-2.1
description: "Deploy poolside's Laguna-S-2.1 — a 118B hybrid-SWA Mixture-of-Experts model (8B active) for agentic coding — with SGLang on NVIDIA H200, B300, and GB300 in BF16, FP8, NVFP4, and INT4."
tag: NEW
---

## Deployment

<a id="install" />

<Accordion title="Install SGLang">

Laguna-S-2.1 uses the same `laguna` model architecture as [Laguna-XS-2.1](./Laguna-XS-2.1), which is fully supported in SGLang `main`. The model ships custom config code on the Hub, so `--trust-remote-code` is required (included in the launch commands).

<Tabs>

<Tab title="Python (pip / uv)">

```bash Command
pip install -U uv
uv venv --python 3.12 && source .venv/bin/activate

git clone https://github.com/sgl-project/sglang.git
cd sglang
uv pip install -e python
```

Then run the **Python** output of the command panel below in that environment.

</Tab>

<Tab title="Docker">

```bash Command
docker pull lmsysorg/sglang:latest
```

For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.

</Tab>

</Tabs>

</Accordion>

Pick your hardware + quantization + strategy to generate the launch command. The two serving strategies cover the common operating points:

- **Low-latency** — DFlash speculative decoding with a matched draft model. Pick for chat and interactive agents.
- **High-throughput** — plain serving. Best for batch workloads, where speculation's draft + rejection overhead costs more than it saves.

On the 8-GPU HGX platforms (H200 / B300) all quantizations run `--tp 8`. The 4-GPU GB300 node runs `--tp 4` throughout. NVFP4 is Blackwell-only (B300 / GB300 only).

import { Deployment } from "/src/snippets/_deployment.jsx";
import { config }     from "/src/snippets/configs/poolside/laguna-s21.jsx";
import { benchmarks } from "/src/snippets/configs/poolside/laguna-s21-benchmarks.jsx";

<Deployment config={config} benchmarks={benchmarks} />

## Playground

The Playground is where you experiment with **SGLang features beyond the verified matrix**. The Deploy panel above only emits combinations that have been signed off; the Playground lets you turn on additional knobs (TP degree, parsers) on top of whichever cell the Deploy panel is currently showing.

import { Playground } from "/src/snippets/_playground.jsx";

<Playground config={config} />

## 1. Model Introduction

[Laguna-S-2.1](https://huggingface.co/poolside/Laguna-S-2.1) is an open-weight **118B-parameter** hybrid sliding-window-attention MoE model (**~8B active per token**) from [poolside](https://poolside.ai), built for agentic coding and long-horizon software engineering. It sits between [Laguna XS 2.1](./Laguna-XS-2.1) (33B/3B active) and Laguna M.1 (222B/23B active) in the Laguna family.

**Key Features:**

- **Sparse MoE**: 48 layers, 256 routed experts, top-10 routing, plus 1 shared expert.
- **Hybrid attention**: 36 sliding-window layers (window 512) interleaved with 12 full-attention layers (1:3 global-to-SWA ratio); 8 KV heads, head dim 128; per-head sigmoid output gating with per-layer-type rotary scales.
- **Long context**: 1,048,576 tokens.
- **DFlash drafts**: matched draft models ship per quantization for low-latency serving.
- **Hybrid reasoning**: `<think>…</think>` toggled per request via `chat_template_kwargs={"enable_thinking": …}`.

**Available quantizations:**

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "14%"}} />
    <col style={{width: "43%"}} />
    <col style={{width: "43%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Precision</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Target model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Draft model</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500}}><strong>BF16</strong></td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1`](https://huggingface.co/poolside/Laguna-S-2.1)</td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-DFlash`](https://huggingface.co/poolside/Laguna-S-2.1-DFlash)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500}}><strong>FP8</strong></td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-FP8`](https://huggingface.co/poolside/Laguna-S-2.1-FP8)</td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-DFlash-FP8`](https://huggingface.co/poolside/Laguna-S-2.1-DFlash-FP8)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500}}><strong>NVFP4</strong></td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-NVFP4`](https://huggingface.co/poolside/Laguna-S-2.1-NVFP4)</td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-DFlash-NVFP4`](https://huggingface.co/poolside/Laguna-S-2.1-DFlash-NVFP4)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500}}><strong>INT4</strong></td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-INT4`](https://huggingface.co/poolside/Laguna-S-2.1-INT4)</td>
      <td style={{padding: "9px 12px"}}>[`poolside/Laguna-S-2.1-DFlash-INT4`](https://huggingface.co/poolside/Laguna-S-2.1-DFlash-INT4)</td>
    </tr>
  </tbody>
</table>

The drafts are small BF16 models, each *calibrated against its quantized target* — always pair a target with its matched draft (mixing precisions degrades accept-length).

**License:** [OpenMDW-1.1](https://openmdw.ai/)

**Resources:** [Hugging Face](https://huggingface.co/poolside/Laguna-S-2.1) · [Technical report](https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-report.pdf) · [API platform](https://platform.poolside.ai)

## 2. Configuration Tips

**Attention backend**

Leave `--attention-backend` unset for High-throughput cells — auto-select is correct (`fa3` on Hopper, `trtllm_mha` on Blackwell). With DFlash active, auto-select instead falls back to `flashinfer`, which breaks this hybrid-SWA model at `tp ≥ 4` on Blackwell (reproduced on Laguna-XS-2.1, greedy GSM8K 76% → 28%), so the Low-latency commands pin the target backend explicitly. Leave `--speculative-draft-attention-backend` unset. Other attention backend choices have not been fully validated on Laguna; keep the default.

**BF16 memory on H200**

BF16 on H200 leaves less headroom for CUDA-graph capture and NCCL allocations than FP8/INT4. The High-throughput BF16 command carries `--mem-fraction-static 0.80`. FP8, INT4, and all B300/GB300 cells use the default heuristic.

**FP8 shared expert**

`SGLANG_SHARED_EXPERT_TP1=1` is required for FP8 cells on **all hardware** — confirmed on both H200 (TP=8) and GB300 (TP=4). The FP8 checkpoint block-quantizes the shared expert (128×128 scales), which cannot TP-shard cleanly at either TP degree on S-2.1. This env var replicates the shared expert instead of sharding it. INT4 keeps the shared expert in BF16 (no flag needed); BF16 is unquantized. Note: this differs from Laguna-XS-2.1 where TP=4 does not require the flag — the constraint is architecture-specific.

**FP8 and NVFP4 DFlash drafts**

Fixed upstream on 2026-07-21: all DFlash draft configs now use a flat top-level `rope_theta` (the `rope_parameters` block was removed). If a server crashes at draft-model load with `KeyError: 'rope_theta'`, you are serving a draft checkpoint cached before 2026-07-21 — re-download it (e.g. `hf download poolside/Laguna-S-2.1-DFlash-FP8`) to pick up the corrected config.

**DFlash memory**

Low-latency cells carry `--mem-fraction-static 0.7` (sufficient even for BF16 on H200). Dense cells use the default heuristic (except BF16 on H200 — see above).

**BF16 reasoning length**

BF16 reasons approximately 2× longer than FP8/INT4 on AIME25 (median 34.8 k vs 16.9 k tokens), consistently truncating at `max_tokens=64000`. FP8/INT4 truncate at ≈ 2%. For a valid BF16 AIME25 score, serve with `max_tokens ≥ 131072` (the model supports a 1 M context window).

**Chat template**

On transformers ≥ 5.10 the standalone `chat_template.jinja` auto-loads — no flag needed (the server logs `Auto-detected template features: reasoning_parser=poolside_v1, ...`). On older transformers (≤ ~5.8) pass `--chat-template <model-dir>/chat_template.jinja` explicitly.

**Thinking**

Off by default; opt in per request with `extra_body={"chat_template_kwargs": {"enable_thinking": True}}`. The template gates on `enable_thinking` — the generic `thinking` key is ignored.

**Served model id**

The server registers the model under whatever you pass to `--model-path`; a client's `model` field must match it (`poolside/Laguna-S-2.1`, or the `-FP8` / `-NVFP4` / `-INT4` id).

## 3. Advanced Usage

### 3.1 DFlash Speculative Decoding

DFlash is a block-wise speculative decoder: the draft proposes a block of tokens and the target verifies the whole block in one forward pass — output quality is the target's by construction. The speedup lever is **accept-length**, the number of draft tokens surviving verification per target step.

Best for interactive / few-stream serving. Under batch-saturated load prefer High-throughput: once the GPU is compute-bound, draft + rejected-token overhead costs aggregate throughput. The generated commands always pair the draft calibrated for the selected target precision.

### 3.2 Reasoning

Launch with `--reasoning-parser poolside_v1` (baked into every generated command). Reasoning is opt-in via `enable_thinking=True`; the `<think>` trace lands in `message.reasoning_content`, separate from the final answer in `message.content`.

<Accordion title="Reasoning Example (Python)">

```python Example
from openai import OpenAI

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

response = client.chat.completions.create(
    model="poolside/Laguna-S-2.1",
    messages=[{"role": "user", "content": "What is 15% of 240? Explain briefly."}],
    max_tokens=4096,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)

message = response.choices[0].message
print("=============== Reasoning ===============")
print(message.reasoning_content)
print("=============== Answer ==================")
print(message.content)
```

</Accordion>

<Note>
Give generous `max_tokens` when thinking is enabled — hard problems regularly reason
for thousands of tokens. Keep thinking off for short-form tasks.
</Note>

### 3.3 Tool Calling

Launch with `--tool-call-parser poolside_v1` (baked into every generated command). The parser converts Laguna's `<tool_call>` output into the standard OpenAI `tool_calls` structure. Tool calling works with reasoning off (the default).

<Accordion title="Tool Calling Example (Python)">

```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"]},
                },
                "required": ["location"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="poolside/Laguna-S-2.1",
    messages=[{"role": "user", "content": "What's the weather in Beijing?"}],
    tools=tools,
)

message = response.choices[0].message
if message.tool_calls:
    for call in message.tool_calls:
        print(f"Tool: {call.function.name}")
        print(f"Args: {call.function.arguments}")
```

</Accordion>
