MiMo-V2-Flash - Sglang

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

# Pull the docker image
docker pull lmsysorg/sglang:dev-pr-15207

# Launch the container
docker run -it --gpus all \
  --shm-size=32g \
  --ipc=host \
  --network=host \
  lmsysorg/sglang:dev-pr-15207 bash

Pip Installation

bash

# 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:nightly-dev-20251215-4449c170 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";

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

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

{
  "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.