# MiMo-V2.5-Pro (Xiaomi 1T MoE) {% hint style="info" %} **Status (April 2026):** MiMo-V2.5-Pro was released on **April 27, 2026** by Xiaomi's AI division as the first open-weight model in their **Pro** tier — the previous MiMo-V2-Pro was API-only with no public weights. Weights live at [huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro](https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro) under the **MIT license**. The model card was last updated April 28, 2026, so deployment tooling, community quants, and reproductions are still landing day-by-day. {% endhint %} MiMo-V2.5-Pro is a **1.02-trillion parameter Mixture-of-Experts** model that activates only **\~42B parameters per token**. The MiMo team — led by ex-DeepSeek researcher **Luo Fuli** — designed it around two ideas: a **hybrid attention scheme** that blends Sliding Window Attention (SWA) and Global Attention (GA) at a 6:1 ratio (\~7× KV-cache reduction with a 128-token window), and **3 lightweight Multi-Token Prediction (MTP) modules** that yield roughly **3× output speed** on autoregressive workloads. The architecture has 70 layers (1 dense + 69 MoE), hidden size 6144, and ships natively in **FP8 E4M3 mixed precision**. Two things matter for Clore.ai users. First, this is the **first MiMo Pro release with public weights**: previous Pro variants only existed as a hosted API and as the stealth-tested "Hunter Alpha" model on OpenRouter (March 2026 timeline). Second, the **MIT license** removes commercial restrictions outright — fine-tune, redistribute, run it as a paid endpoint, no caveats. Xiaomi's launch announcement claims V2.5-Pro **beats DeepSeek V4 on agentic tasks**, but that benchmark is vendor-published only — third-party reproduction has not landed yet, and you should not quote it externally without that caveat. ### Key Specs | Property | Value | | -------------------- | ---------------------------------------------------------------- | | Total Parameters | 1.02T (MoE) | | Active Parameters | \~42B per forward pass | | Context Window | 1,000,000 tokens (1M) | | Precision | FP8 E4M3 mixed (native) | | Architecture | Hybrid SWA + GA (6:1), 70 layers (1 dense + 69 MoE), hidden 6144 | | KV-Cache | Sliding window 128, \~7× reduction vs full GA | | Speculative Decoding | 3 lightweight MTP modules, \~3× output speed | | License | MIT | | Release Date | April 27, 2026 | | Organization | Xiaomi MiMo team (XiaomiMiMo on HuggingFace) | | Primary Tooling | SGLang (first-class), vLLM | ### Why MiMo-V2.5-Pro? * **First open Pro-tier MiMo** — predecessor MiMo-V2-Pro was API-only, this is the first time the Pro weights are public * **1M-token context** — handles entire codebases, long agent traces, or multi-document RAG without chunking * **Hybrid attention** — SWA + GA at 6:1 cuts KV-cache \~7× vs pure global attention; long contexts stay tractable * **Native FP8** — no post-hoc quantization, weights ship in FP8 E4M3 directly from the vendor * **MTP speculative decoding** — 3 built-in MTP modules give \~3× decode throughput out of the box * **MIT license** — no commercial restrictions, no field-of-use limits * **42B active** — you pay 42B-dense inference cost despite the 1.02T headline number * **Lineage** — lead researcher Luo Fuli was previously at DeepSeek, and the architectural choices show *** ## Requirements {% hint style="warning" %} **Still a 1T model.** "42B active" sounds friendly, but the full 1.02T weights must live in VRAM (or be aggressively offloaded). Native FP8 weights need **\~600GB+ VRAM** before activation memory and KV cache. Plan for 8×H200 or larger for full-context FP8. {% endhint %} | Component | Minimum (Quant + offload, future) | Recommended (FP8) | Full FP8, 1M ctx | | --------- | -------------------------------------------- | -------------------- | ----------------------- | | GPU VRAM | \~141GB (Q4 + RAM offload, when quants land) | 8× H100 80GB (640GB) | 8× H200 141GB (1,128GB) | | RAM | 256GB | 512GB | 512GB | | Disk | 700GB NVMe | 1.5TB NVMe | 2TB NVMe | | CUDA | 12.4+ | 12.6+ | 12.6+ | **Clore.ai pick:** For full FP8 with breathing room on the 1M context, **8×H200** is the natural fit — see [clore.ai/rent-h200.html](https://clore.ai/rent-h200.html). 8×H100 80GB also runs the FP8 checkpoint but you'll cap `--context-length` lower (typically 256K) to leave room for KV cache. For Blackwell-class hardware see [clore.ai/rent-b200.html](https://clore.ai/rent-b200.html). *** ## Option A — Ollama / GGUF (Quantized, community builds) {% hint style="warning" %} **Heads-up:** As of April 28, 2026 (one day after release) **community GGUF quants for MiMo-V2.5-Pro are not yet published**. Expect Q4\_K\_M / Q5\_K\_M / Q6\_K builds to appear within 1–2 weeks at [huggingface.co/models?search=mimo-v2.5-pro+gguf](https://huggingface.co/models?search=mimo-v2.5-pro+gguf). Until then, FP8 via SGLang or vLLM is the supported path. {% endhint %} ```bash # Once a Q4_K_M build is available docker exec ollama ollama pull mimo-v2.5-pro:q4_K_M docker exec ollama ollama run mimo-v2.5-pro:q4_K_M # Or with llama.cpp directly on a GGUF file (when published) docker run --gpus all -it --rm -p 8080:8080 \ -v $(pwd)/models:/models \ ghcr.io/ggerganov/llama.cpp:server-cuda \ -m /models/mimo-v2.5-pro-q4_k_m.gguf \ --n-gpu-layers 99 --ctx-size 65536 \ --port 8080 --host 0.0.0.0 ``` *** ## Option B — vLLM (Production API, recommended) vLLM supports MiMo-V2.5-Pro via `--trust-remote-code` (the hybrid attention + MTP modules ship as custom code in the repo). Use the vendor sampling defaults: **temperature 1.0, top\_p 0.95**. ```yaml version: "3.8" services: vllm: image: vllm/vllm-openai:latest ports: - "8000:8000" volumes: - hf_cache:/root/.cache/huggingface command: > --model XiaomiMiMo/MiMo-V2.5-Pro --tensor-parallel-size 8 --quantization fp8 --max-model-len 262144 --gpu-memory-utilization 0.90 --trust-remote-code --served-model-name mimo-v2.5-pro deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: [gpu] shm_size: "16gb" volumes: hf_cache: ``` ```bash # Test the API (vendor-recommended sampling) curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "mimo-v2.5-pro", "messages": [ {"role": "system", "content": "You are an autonomous coding agent."}, {"role": "user", "content": "Walk through this 30K-line monorepo and propose a migration plan from Express 4 to Fastify 5."} ], "max_tokens": 8192, "temperature": 1.0, "top_p": 0.95 }' ``` {% hint style="info" %} On 8×H100 80GB, cap `--max-model-len` at 262144 (256K) to leave headroom for activations + KV cache. On 8×H200 141GB you can comfortably push to 524288 or higher; 1,048,576 (full 1M) is feasible but expect long prefill times — test before relying on it. {% endhint %} *** ## Option C — SGLang (recommended for max throughput) SGLang is the **first-class serving target** in the MiMo-V2.5-Pro model card. The vendor publishes the launch command with **`SGLANG_ENABLE_SPEC_V2=1`** to activate the new MTP-aware speculative decoding path, which is where the \~3× decode speedup actually materializes. ```bash docker pull lmsysorg/sglang:latest # Verbatim from the HF model card SGLANG_ENABLE_SPEC_V2=1 python3 -m sglang.launch_server \ --model-path XiaomiMiMo/MiMo-V2.5-Pro \ --trust-remote-code \ --quantization fp8 \ --context-length 1048576 \ --host 0.0.0.0 --port 9001 ``` For a multi-GPU TP setup on 8×H200, add `--tp-size 8` and `--mem-fraction-static 0.88`. Confirm with `nvidia-smi` that all 8 cards are populated before sending real traffic — the 1M context is unforgiving if one rank is starved. *** ## Clore.ai GPU Recommendations | Setup | VRAM | Expected Performance | Clore.ai Cost | | ------------- | ------- | ---------------------------------------------------- | ------------------- | | 4× H100 80GB | 320GB | FP8 with heavy offload, max ctx \~64K, \~10–15 tok/s | \~$25–35/day | | 8× H100 80GB | 640GB | FP8 full, max ctx \~256K, \~30–45 tok/s | \~$45–60/day | | 8× H200 141GB | 1,128GB | FP8 full, max ctx 1M, \~60+ tok/s with MTP | \~$80–110/day | | 8× B200 | 1,536GB | FP8 full, max ctx 1M, fastest available | marketplace pricing | {% hint style="success" %} **Best value:** 8× H200 141GB on the FP8 checkpoint with `SGLANG_ENABLE_SPEC_V2=1`. You get the full 1M context window, MTP speculative decoding, and enough KV-cache headroom for real agent loops. See [clore.ai/rent-h200.html](https://clore.ai/rent-h200.html) for live availability. {% endhint %} *** ## Use Cases * **Long-horizon agents** — MiMo team explicitly tunes for sustained tool-calling. The 1M context plus MTP speedup means thousands of tool turns without chunking gymnastics. * **Whole-codebase analysis** — drop a 500K-token monorepo into context for refactor planning, dependency audits, or migration design * **Long-document RAG** — entire books, multi-year customer transcripts, or year-long chat histories fit in one prompt * **Coding** — vendor-claimed HumanEval+ 75.6% and the agentic posture make it a candidate for autonomous SWE workloads (pair with SWE-agent / OpenHands) * **Research scratchpad** — 1M context tolerates the kind of "paste the whole paper, paste the prior work, ask for synthesis" usage that smaller models truncate *** ## Benchmarks {% hint style="warning" %} **Vendor-claimed — no third-party reproduction yet.** All numbers below come from Xiaomi's April 27, 2026 announcement and the HuggingFace model card. The model is **two days old** at time of writing — independent reproductions on agentic and long-context benchmarks are still pending. The "beats DeepSeek V4 on agentic tasks" claim in particular is from Xiaomi's own write-up; treat it as marketing until reproduced. {% endhint %} | Benchmark | MiMo-V2.5-Pro (vendor) | Notes | | ---------------------------- | ---------------------- | ------------------------------------------------- | | GSM8K | **99.6%** | Math word problems | | HumanEval+ | 75.6% | Coding (extended) | | MMLU | 89.4% | General knowledge | | GraphWalks (1M ctx) BFS | 0.37 | Long-context graph traversal | | GraphWalks (1M ctx) Parents | 0.62 | Long-context graph traversal | | Agentic tasks vs DeepSeek V4 | "outperforms" (vendor) | **Unverified — third-party reproduction pending** | *** ## Troubleshooting | Issue | Solution | | ----------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | | `OutOfMemoryError` on load | Native FP8 still needs \~600GB+ VRAM. Use 8× H200 or drop `--context-length` to 65536 on 8× H100. | | Slow HuggingFace download | `huggingface-cli download XiaomiMiMo/MiMo-V2.5-Pro --local-dir ./weights --resume-download`. Expect \~600GB FP8. | | `--trust-remote-code` rejected | Hybrid attention and MTP ship as custom code in the repo. The flag is **mandatory** for both vLLM and SGLang. | | MTP speedup not appearing in SGLang | Confirm `SGLANG_ENABLE_SPEC_V2=1` is exported in the same shell as `python3 -m sglang.launch_server`. The default path does not activate MTP. | | Reasoning trace flat / low quality | Use `temperature=1.0` and `top_p=0.95`. Lower temps degrade MiMo's reasoning behavior. | | 1M context OOMs on 8× H100 | 8× H100 80GB cannot hold KV cache for 1M tokens. Cap at 256K or move to 8× H200. | | Prefill takes minutes | Expected at 1M context. Use `--enable-chunked-prefill` (vLLM) or batch shorter requests for interactive workloads. | | GGUF / Ollama pull fails | Community quants are not published as of April 28, 2026. Wait 1–2 weeks or use FP8 directly. | *** ## Next Steps * **Predecessor / sibling:** [MiMo-V2-Flash](/guides/language-models/mimo-v2-flash.md) — 309B MoE, 15B active, 32K ctx, faster but smaller * **Vendor's claimed rival:** [DeepSeek V4](/guides/language-models/deepseek-v4.md) — 1M ctx, multimodal, \~1T params (the model Xiaomi says they beat on agentic tasks) * **Open-weight coding rival:** [GLM-5.1](/guides/language-models/glm-5-1.md) — 744B MoE, 40B active, MIT, currently #1 on SWE-Bench Pro * **Clore.ai H200 rentals:** [clore.ai/rent-h200.html](https://clore.ai/rent-h200.html) — best fit for full FP8 1T MoE at 1M context * **Clore.ai marketplace:** [clore.ai/marketplace](https://clore.ai/marketplace) ### Links * [MiMo-V2.5-Pro on HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro) * [Xiaomi MiMo HuggingFace org](https://huggingface.co/XiaomiMiMo) * [SGLang repo](https://github.com/sgl-project/sglang) * [vLLM docs](https://docs.vllm.ai) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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