MiMo-V2-Flash
MiMo-V2-Flash is a 309B-parameter mixture-of-experts language model from Xiaomi with only 15B active parameters, designed for fast inference and agentic reasoning tasks. It uses a hybrid attention architecture and multi-token prediction to reduce computational overhead while maintaining competitive performance on standard benchmarks. The model is MIT-licensed, ungated, and available on HuggingFace.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | XiaomiMiMo |
| Parameters | 309.8B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 66.1k |
| Likes | 743 |
| Last updated | 2026-04-20 |
| Source | XiaomiMiMo/MiMo-V2-Flash |
What MiMo-V2-Flash is
MiMo-V2-Flash is a MoE architecture with 309B total parameters and 15B active parameters, trained on 27T tokens in FP8 mixed precision. It features: (1) hybrid attention interleaving sliding-window (128-token) and global attention at 5:1 ratio with learnable attention sink bias, reducing KV-cache by ~6x; (2) lightweight multi-token prediction module (0.33B params/block) for 3x faster inference; (3) native 32k context with 256k capability; (4) post-training via Multi-Teacher On-Policy Distillation and large-scale agentic RL. Evaluation shows competitive or superior performance on standard benchmarks (MMLU, GSM8K, code) versus larger models like DeepSeek-V3 and Kimi-K2, with particular strength on AIME, SWE-Bench, and long-context tasks.
Run MiMo-V2-Flash locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2-Flash")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~30–40 GB VRAM for FP8 inference (15B active params × 2 bytes + KV-cache + overhead). Full precision inference would require 60+ GB. Multi-GPU setup recommended for production (<100ms latency). Requires recent NVIDIA H100/A100 or equivalent for efficient serving. Exact requirements depend on batch size, context length, and serving framework; vendor documentation should be reviewed.
Not explicitly stated in card. MoE architectures typically support LoRA on expert FFNs and query/key projections, but custom-code requirement suggests non-standard layer structure. Feasibility of QLoRA or parameter-efficient fine-tuning Unknown without architecture details. Recommend: (1) consult GitHub repository for training code, (2) test LoRA on base model before production tuning, (3) assess activation sparsity impact on LoRA gradient flow.
When to avoid it — and what to weigh
- Real-time edge inference on resource-constrained devices — Despite efficiency gains, 15B active parameters still requires substantial VRAM (estimated 30–40 GB in FP8). Not suitable for mobile, IoT, or single-GPU edge deployment without quantization beyond FP8.
- Production use without custom integration testing — Model card lists 'custom_code' tag, indicating non-standard architecture components. Deployment via standard frameworks (vLLM, TGI) requires verification; vendor-specific tooling may be necessary.
- Use cases requiring strict Chinese language performance — On Chinese benchmarks (C-Eval, CMMLU, C-SimpleQA), performance lags Kimi-K2 and DeepSeek by 3–16 percentage points. English and code tasks are stronger relative to scale.
- Scenarios with unknown hardware availability — Serves as a large model that requires enterprise-grade infrastructure (multi-GPU, high-bandwidth interconnect) for production. Smaller quantized alternatives may be preferable for constrained environments.
License & commercial use
MIT license. This is a permissive OSI-approved license that permits commercial use, modification, and distribution provided the license and copyright notice are retained.
MIT license explicitly permits commercial use without restriction. However, deployment of 309B-parameter model incurs significant infrastructure costs (GPUs, bandwidth). Verify with legal team: (1) liability indemnification in vendor terms of service; (2) model weight distribution compliance if bundling into proprietary product; (3) absence of export restrictions on Xiaomi-developed artifacts. No gatekeeping or usage restrictions observed on HuggingFace, but review Xiaomi MiMo API Platform's commercial terms if using managed hosting.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Not stated in card. General considerations: (1) model weights downloaded from HuggingFace should be verified against official SHA/PGP signatures if available (check GitHub); (2) 'custom_code' tag requires code review before execution in production—audit any imports and dynamic behavior; (3) no mention of adversarial robustness testing or jailbreak evaluation; (4) long-context capability (256k tokens) may increase prompt-injection surface area; (5) review Xiaomi privacy policy if using managed API Platform. Do not assume the model is adversarially robust or free of biases without independent evaluation.
Alternatives to consider
DeepSeek-V3 (Base or Exp variants)
Similar scale (37B active/671B total) with stronger Chinese performance and comparable math/code scores. More established ecosystem and broader deployment support. Trade-off: less aggressive efficiency gains on English benchmarks.
Kimi-K2
32B active/1043B total with best-in-class Chinese and conversational capabilities. Stronger on HellaSwag, SimpleQA, TriviaQA. Trade-off: higher parameter count, no public release roadmap, potential licensing constraints from Zhipu AI.
Llama 3.1 405B (or quantized variants)
Fully open-source, well-established fine-tuning ecosystem, native support across all major inference frameworks. Trade-off: significantly larger (405B), no MoE efficiency gains, lower performance on some benchmarks like AIME but stronger on code.
Ship MiMo-V2-Flash with senior software developers
Download the model from HuggingFace, test on your target hardware, and review the technical report. Consult the GitHub repository for architecture details and fine-tuning guidance. Contact Devco for infrastructure sizing and custom integration support.
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MiMo-V2-Flash FAQ
Can I use MiMo-V2-Flash in a commercial product?
What GPU do I need to run this model?
How does MiMo-V2-Flash compare to GPT-4 or Claude?
Does it support fine-tuning or LoRA?
Work with a software development agency
Need help beyond evaluating MiMo-V2-Flash? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Evaluate MiMo-V2-Flash for Your Use Case
Download the model from HuggingFace, test on your target hardware, and review the technical report. Consult the GitHub repository for architecture details and fine-tuning guidance. Contact Devco for infrastructure sizing and custom integration support.