MiMo-V2.5-Pro
MiMo-V2.5-Pro is a 1.02T-parameter Mixture-of-Experts language model from Xiaomi with 42B active parameters, designed for long-context reasoning (up to 1M tokens) and agentic tasks. It uses a hybrid attention architecture combining sliding-window and global attention, plus multi-token prediction for faster inference. The model is MIT-licensed, ungated, and available on HuggingFace. It shows strong performance on math, code, and long-context benchmarks, particularly excelling at complex reasoning tasks and software engineering problems.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | XiaomiMiMo |
| Parameters | 1023.2B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 101.7k |
| Likes | 698 |
| Last updated | 2026-05-08 |
| Source | XiaomiMiMo/MiMo-V2.5-Pro |
What MiMo-V2.5-Pro is
MiMo-V2.5-Pro is a MoE-based decoder-only language model with 70 layers (1 dense + 69 MoE), 384 routed experts (8 per token), hidden size 6144, 128 attention heads, and GQA. Key architectural innovations: (1) Hybrid attention—60 sliding-window layers (128-token window) + 10 global attention layers in 6:1 ratio, reducing KV-cache by ~7x; (2) Three native multi-token prediction modules using dense FFNs for 3x output speed during inference; (3) FP8 (E4M3) mixed precision training on 27T tokens with native 32k sequence length, supporting up to 1M via position interpolation. Post-training uses three-stage approach: SFT, domain-specialized RL with expert teachers, and multi-teacher on-policy distillation (MOPD). Evaluation shows competitive performance on MMLU (89.4), GSM8K (99.6), and strong long-context retention (0.37 BFS / 0.62 Parents at 1M tokens on GraphWalks).
Run MiMo-V2.5-Pro locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.5-Pro")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—Verification recommended: FP8 mixed precision inference on 42B active parameters + KV-cache suggests ~80–150GB VRAM for dense batch inference (single batch, no quantization). Typical multi-GPU setup: 8× H100/A100 or 16–32× L40S/L40G. Single-GPU inference requires post-training quantization (INT8, GPTQ) reducing to ~40–60GB, with latency tradeoff. Training would require 64–256 GPUs depending on batch size. SGLang deployment example shows `--tp-size 16 --ep-size 16` for production serving, implying ~16-GPU minimum for reasonable throughput.
LoRA/QLoRA feasibility on MoE architecture is not explicitly documented. The 1.02T parameter count makes full fine-tuning impractical without industrial-scale infrastructure. Model card references domain-specific RL and MOPD multi-teacher distillation, suggesting post-training was complex, but no guidance on user-level adaptation is provided. Supervised fine-tuning via direct instruction-following appears possible given public SGLang deployment examples, but parameter-efficient tuning support is Unknown. Recommend contacting Xiaomi for LoRA compatibility and expert router stability during fine-tuning.
When to avoid it — and what to weigh
- Latency-Critical Consumer Applications — MoE routing overhead and 1.02T total parameters require significant computational resources per inference. Multi-token prediction helps but does not eliminate latency penalty vs. smaller dense models. Not suitable for sub-100ms SLA requirements on standard hardware.
- Resource-Constrained Environments — Even with 42B active parameters, sparse activation does not eliminate absolute VRAM and bandwidth requirements. Requires multi-GPU/multi-node deployment in most scenarios. Unsuitable for edge devices, mobile, or single-GPU inference without aggressive quantization.
- Safety-Critical or Heavily Regulated Domains — No explicit safety audits, red-teaming results, or regulatory compliance details provided in model card. Post-training mentions 'safety teacher' but methodology and guarantees are not disclosed. Requires independent evaluation before deployment in healthcare, finance, legal, or autonomous systems.
- Low-Resource Fine-Tuning — 1.02T parameter count makes full fine-tuning prohibitively expensive. LoRA/QLoRA feasibility on MoE architecture is unclear and not documented. Suitable only for organizations with substantial compute budgets for adaptation.
License & commercial use
MIT License (OSI-compliant, permissive). MIT allows unrestricted use, modification, and distribution, including commercial deployment, provided the license notice and copyright claim are retained. No additional restrictions stated on model card.
MIT license explicitly permits commercial use without royalties or additional licensing. No gating, API-only restrictions, or commercial partnership requirements stated. Model weights are publicly downloadable on HuggingFace and ModelScope. No affiliation or profit-sharing clause mentioned. Commercial use is clear from license standpoint. However, verify with Xiaomi if model output or derived models trigger any Xiaomi brand/trademark restrictions outside the license itself.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, red-teaming results, jailbreak resistance testing, or safety-tuning methodology disclosed. Model card mentions 'safety teacher' in MOPD training but does not detail safety constraints or guardrails. No disclosure of training data filtering for harmful content, bias audits, or RLHF safety alignment methodology. FP8 precision raises potential concerns for floating-point attacks, but no security analysis provided. Custom code trust required (model card shows `trust-remote-code` flag in deployment). Recommend independent red-teaming and responsible disclosure protocol before high-stakes deployment.
Alternatives to consider
DeepSeek-V4-Pro
Comparable 1.6T parameter MoE (49B active) with similar long-context and agentic focus. MMLU 90.1 (vs. MiMo 89.4), MATH 64.5 (vs. 86.2). Strengths: proven DeepSeek ecosystem; potential commercial backend. Weaknesses: unknown licensing model as of data cutoff, no published GraphWalks long-context evals.
Kimi-K2 (Moonshot AI)
1.04T parameter MoE (32B active) with 200k+ context claimed. MMLU-Pro 69.2 (vs. MiMo 68.5), HumanEval+ 84.8 (vs. MiMo 75.6). Strengths: proven long-context scaling in production; multilingual support. Weaknesses: availability/licensing unclear, limited base model eval disclosure, no open-source weights.
Llama 3.3 70B (Dense)
70B dense parameters, MIT license, well-documented, lower deployment friction. Benchmarks: MMLU 85.2 (vs. MiMo 89.4). Strengths: simple architecture, lower memory per token, stronger software ecosystem (vLLM, ollama.cpp). Weaknesses: short context (8k), no agent tuning, single-digit code benchmarks.
Ship MiMo-V2.5-Pro with senior software developers
MiMo-V2.5-Pro is ideal for teams building agent systems, long-context document analysis, or complex code workflows. Evaluate feasibility with our hardware calculator, review SGLang deployment best practices, and contact Xiaomi's community for production support. Start with a 16-GPU pilot on HuggingFace weights—no licensing friction.
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MiMo-V2.5-Pro FAQ
Can I use MiMo-V2.5-Pro commercially?
What is the recommended GPU setup for inference?
Does MiMo-V2.5-Pro support fine-tuning or LoRA?
How does the 1M token context actually work in practice?
Custom software development services
DEV.co helps companies turn open-source tools like MiMo-V2.5-Pro into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy MiMo-V2.5-Pro?
MiMo-V2.5-Pro is ideal for teams building agent systems, long-context document analysis, or complex code workflows. Evaluate feasibility with our hardware calculator, review SGLang deployment best practices, and contact Xiaomi's community for production support. Start with a 16-GPU pilot on HuggingFace weights—no licensing friction.