---
title: MiMo-V2-Flash
metatags:
    description: "Deploy MiMo-V2-Flash 309B MoE model with SGLang - hybrid attention, multi-token prediction, and 256K context for efficient inference."
---

## Introduction

XiaomiMiMo/MiMo-V2-Flash, with 309B total parameters and 15B activated parameters, is a new inference-centric model designed to maximize decoding efficiency created by XiaomiMiMo Team explicitly co-designed for real-world serving workloads, enabling flexible tradeoffs between throughput and latency on different hardware.

This model creates a new balance between long-context modeling capability and inference efficiency. Key features include:
- **Hybrid Attention Architecture**: Interleaves Sliding Window Attention (SWA) and Global Attention (GA) with a 5:1 ratio and an aggressive 128-token window. This reduces KV-cache storage by nearly 6x while maintaining long-context performance via learnable attention sink bias.
- **Multi-Token Prediction (MTP)**: Equipped with a lightweight MTP module (0.33B params/block) using dense FFNs. This triples output speed during inference and will be good to accelerates rollout in RL training.
- **Efficient Pre-Training**: Trained on 27T tokens using FP8 mixed precision and native 32k seq length. The context window supports up to 256k length.
- **Agentic Capabilities**: Post-training utilizes Multi-Teacher On-Policy Distillation (MOPD) and large-scale agentic RL, achieving superior performance on SWE-Bench and complex reasoning tasks.


## Installation

MiMo-V2-Flash is currently available in SGLang via Docker image and pip install.

### Docker

```bash Command
# Pull the docker image
docker pull lmsysorg/sglang:latest

# Launch the container
docker run -it --gpus all \
  --shm-size=32g \
  --ipc=host \
  --network=host \
  lmsysorg/sglang:latest bash
```

### Pip Installation

```bash Command
# On a machine with SGLang dependencies installed or inside a SGLang nightly container
# Start an SGLang nightly container
docker run -it --gpus all \
  --shm-size=32g \
  --ipc=host \
  --network=host \
  lmsysorg/sglang:latest bash

# If you already have SGLang installed, uninstall the current SGLang version
pip uninstall sglang -y

# Install the PyPI Package
pip install sglang==0.5.6.post2.dev8005+pr.15207.g39d5bd57a \
  --extra-index-url https://sgl-project.github.io/whl/pr/
```

## Model Deployment

Use the configuration selector below to automatically generate the appropriate deployment command.

import { MiMoV2FlashDeployment } from "/src/snippets/autoregressive/mimo-v2-flash-deployment.jsx";

<MiMoV2FlashDeployment />

MI355X (ROCm) is validated in the selector above with `--tp-size 4`, Triton attention, and `--disable-custom-all-reduce`. `--tp-size 8` hit a QKV sharding error during validation. EAGLE speculative decoding is still WIP on MI355X.

## Testing the deployment

Once the server is running, test it with a chat completion request in another terminal:

```bash Command
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "XiaomiMiMo/MiMo-V2-Flash",
    "messages": [
      {"role": "user", "content": "Hello! What can you help me with?"}
    ],
    "temperature": 0.7,
    "max_tokens": 100
  }'
```

**Expected response:**

```json Config
{
  "id": "...",
  "object": "chat.completion",
  "model": "XiaomiMiMo/MiMo-V2-Flash",
  "choices": [{
    "message": {
      "role": "assistant",
      "content": "Hello! I can help you with..."
    }
  }]
}
```

## Troubleshooting

**DeepGEMM Timeout Error**

Occasionally DeepGEMM timeout errors occur during first launch. Simply rerun the server command in the same container - the compiled kernels are cached and subsequent launches will be fast.

**ROCm MI355X Attention Backend**

If you see an error such as `AiterAttnBackend.forward_decode() got an unexpected keyword argument 'sinks'` on MI355X, use the `MI355X` + `Performance Optimizations` command from the selector above, which switches to Triton attention and keeps `--disable-custom-all-reduce`.
