MiMo-V2-Flash

Deploy MiMo-V2-Flash (309B MoE) with speculative decoding on Clore.ai — ultra-fast inference with 150+ tok/s

MiMo-V2-Flash is a 309-billion parameter Mixture-of-Experts language model that activates 15B parameters per token. Built with advanced speculative decoding (EAGLE/MTP), it delivers 150+ tokens/second on 8×H100 while maintaining frontier-level performance. Released under MIT license, it represents the cutting edge of efficient large-scale inference.

At a Glance

• Model Size: 309B total / 15B active parameters (MoE)

• License: MIT (fully commercial)

• Context: 32K tokens

• Performance: State-of-the-art on reasoning benchmarks

• VRAM: ~320GB FP16 (minimum 4×A100 80GB)

• Speed: 150+ tok/s on 8×H100 with speculative decoding

Why MiMo-V2-Flash?

Breakthrough Speed: MiMo-V2-Flash achieves unprecedented inference speeds through EAGLE (Extrapolation Algorithm for Greater Language model Efficiency) and MTP (Multi-Token Prediction). Where traditional models generate one token at a time, MiMo-V2 predicts and validates multiple tokens in parallel.

Production-Ready Scale: At 309B parameters, MiMo-V2-Flash competes with the largest frontier models while remaining deployable on realistic hardware configurations. The 15B active parameters ensure efficient inference despite the massive parameter count.

Advanced Architecture: Beyond standard MoE, MiMo-V2-Flash incorporates speculative decoding natively in the model architecture. This isn't a post-training optimization — it's built into the foundation, enabling guaranteed speedups.

Enterprise Quality: MIT licensing with no usage restrictions. Deploy at scale, fine-tune, or integrate into commercial products without licensing concerns.

GPU Recommendations

Setup	VRAM	Performance	Daily Cost*
4×A100 80GB	320GB	~80 tok/s	~$16.00
8×A100 40GB	320GB	~70 tok/s	~$28.00
2×H100	160GB	~90 tok/s	~$12.00
8×H100	640GB	150+ tok/s	~$48.00
4×H200	564GB	~120 tok/s	~$32.00

Best Value: 4×A100 80GB provides excellent performance per dollar. Maximum Performance: 8×H100 unleashes full speculative decoding potential.

*Estimated Clore.ai marketplace prices

Deploy with SGLang (Recommended)

SGLang provides the best support for MiMo-V2-Flash's speculative decoding features:

Install SGLang

pip install "sglang[all]>=0.3.0"
# or latest
pip install git+https://github.com/sgl-project/sglang.git

Multi-GPU Setup with MTP

python -m sglang.launch_server \
  --model-path mimo-ai/MiMo-V2-Flash \
  --tp-size 8 \
  --enable-mtp \
  --mtp-max-draft-tokens 8 \
  --mtp-acceptance-rate 0.8 \
  --mem-fraction-static 0.85 \
  --dtype float16 \
  --context-length 32768 \
  --served-model-name mimo-v2-flash

Query with OpenAI API

from openai import OpenAI

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

response = client.chat.completions.create(
    model="mimo-v2-flash",
    messages=[
        {"role": "system", "content": "You are an expert AI researcher."},
        {"role": "user", "content": "Explain the EAGLE speculative decoding algorithm and why it enables faster inference"}
    ],
    max_tokens=1024,
    temperature=0.7,
    stream=True  # Recommended for best latency
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end='', flush=True)

Deploy with vLLM

vLLM also supports MiMo-V2-Flash with speculative decoding:

pip install vllm>=0.6.0

vllm serve mimo-ai/MiMo-V2-Flash \
  --tensor-parallel-size 8 \
  --speculative-model mimo-ai/MiMo-V2-Flash-Draft \
  --speculative-max-model-len 32768 \
  --speculative-draft-tensor-parallel-size 2 \
  --use-v2-block-manager \
  --dtype float16 \
  --served-model-name mimo-v2-flash \
  --trust-remote-code

Docker Template

FROM nvidia/cuda:12.1-devel-ubuntu22.04

# Install dependencies
RUN apt-get update && \
    apt-get install -y python3.10 python3-pip git && \
    rm -rf /var/lib/apt/lists/*

# Install SGLang with MTP support
RUN pip install "sglang[all]>=0.3.0" transformers

# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

# Pre-download model (optional, saves startup time)
# RUN python3 -c "from transformers import AutoModel; AutoModel.from_pretrained('mimo-ai/MiMo-V2-Flash', trust_remote_code=True)"

EXPOSE 30000

CMD ["python", "-m", "sglang.launch_server", \
     "--model-path", "mimo-ai/MiMo-V2-Flash", \
     "--host", "0.0.0.0", \
     "--port", "30000", \
     "--tp-size", "8", \
     "--enable-mtp", \
     "--mtp-max-draft-tokens", "8", \
     "--dtype", "float16"]

Run with all GPUs:

docker build -t mimo-v2-flash .
docker run --gpus all -p 30000:30000 \
  --shm-size=64g \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  mimo-v2-flash

Advanced Configuration

Optimizing Speculative Decoding

Fine-tune speculative parameters based on your workload:

# For code generation (higher acceptance rate)
python -m sglang.launch_server \
  --model-path mimo-ai/MiMo-V2-Flash \
  --tp-size 8 \
  --enable-mtp \
  --mtp-max-draft-tokens 12 \
  --mtp-acceptance-rate 0.9 \
  --temperature 0.1

# For creative writing (lower acceptance rate)
python -m sglang.launch_server \
  --model-path mimo-ai/MiMo-V2-Flash \
  --tp-size 8 \
  --enable-mtp \
  --mtp-max-draft-tokens 6 \
  --mtp-acceptance-rate 0.7 \
  --temperature 0.8

Memory Optimization

For memory-constrained setups:

# Reduce memory usage (slower but fits 4×A100)
python -m sglang.launch_server \
  --model-path mimo-ai/MiMo-V2-Flash \
  --tp-size 4 \
  --mem-fraction-static 0.75 \
  --context-length 16384 \
  --dtype float16 \
  --disable-cuda-graph  # Saves VRAM

Benchmarking Example

Test MiMo-V2-Flash's speed advantage:

import time
from openai import OpenAI

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

def benchmark_generation():
    start_time = time.time()
    
    response = client.chat.completions.create(
        model="mimo-v2-flash",
        messages=[
            {"role": "user", "content": "Write a detailed explanation of quantum computing in exactly 500 words"}
        ],
        max_tokens=600,
        temperature=0.1,
        stream=False
    )
    
    end_time = time.time()
    content = response.choices[0].message.content
    
    tokens = len(content.split())  # Rough token estimate
    duration = end_time - start_time
    tokens_per_second = tokens / duration
    
    print(f"Generated {tokens} tokens in {duration:.2f}s")
    print(f"Speed: {tokens_per_second:.1f} tokens/second")
    
    return tokens_per_second

# Run benchmark
speed = benchmark_generation()
print(f"\nMiMo-V2-Flash achieved {speed:.1f} tok/s")

Tips for Clore.ai Users

• Multi-GPU Essential: MiMo-V2-Flash requires minimum 4×A100 80GB. Single-GPU deployment isn't feasible.

• NVLink Advantage: Choose Clore.ai hosts with NVLink between GPUs for optimal multi-GPU communication.

• RAM Requirements: Ensure 256GB+ system RAM for smooth operation with 8 GPUs.

• Speculative Tuning: Adjust mtp-max-draft-tokens based on your use case — higher for repetitive tasks, lower for creative work.

• Context Length: 32K context is optimal. Longer contexts reduce speculative decoding effectiveness.

Troubleshooting

Issue	Solution
OutOfMemoryError on startup	Reduce mem-fraction-static or tp-size
Slow inter-GPU communication	Verify NVLink: nvidia-ml-py3 or nvidia-smi topo -m
MTP not accelerating	Check mtp-acceptance-rate — too high values disable speculation
Model loading timeout	Pre-download: huggingface-cli download mimo-ai/MiMo-V2-Flash
Poor token acceptance	Verify temperature settings — very low/high temps reduce acceptance

Performance Comparison

Model	Size	Speed (8×H100)	Quality
GPT-4 Turbo	~1.7T	~15-25 tok/s	★★★★★
Claude Sonnet 3.5	~200B	~25-35 tok/s	★★★★★
MiMo-V2-Flash	309B	150+ tok/s	★★★★☆
Llama 3.1 405B	405B	~30-45 tok/s	★★★★☆

MiMo-V2-Flash achieves 3-5x speedup over comparable models while maintaining competitive quality.

Resources

• MiMo-V2-Flash on Hugging Face

• EAGLE Paper

• SGLang Documentation

• Multi-Token Prediction

• Speculative Decoding Guide