MiniMax-M2 VRAM Requirements

MiniMax-M2 at a glance

Made by

MiniMax, China

Released

October 22, 2025

Parameters

228.7B, 11B active

Context window

192K tokens

License

Modified MIT

On Hugging Face

1.5K likes, 232K downloads last month (opens in a new tab)

Runs with

vLLM, SGLang, llama.cpp (Ollama, LM Studio), MLX (Mac)

Checked against

KV cache matches the GGUF header of unsloth/MiniMax-M2-GGUF (opens in a new tab) (what llama.cpp allocates), October 11, 2026

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

MiniMax-M2 GGUF files

Exact size of each quantization in unsloth/MiniMax-M2-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run MiniMax-M2

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size. —: the official FP8 weights do not run there: llama.cpp needs a GGUF version, and vLLM needs a GPU with FP8 support.

Cheapest way to run MiniMax-M2 today

For each GPU: the fewest cards that fit MiniMax-M2 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-11).

On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

KV cache: how memory grows with context

Architecture: 62 layers · 62 full attention (8 KV × 128). Every layer keeps keys and values for every token (grouped-query attention), so the cache grows linearly with context. Only 11B parameters are active per token, but all 229B must sit in memory: VRAM depends on the total, speed on the active part.

MiniMax-M2 VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/minimax-m2.json.

[![MiniMax-M2 VRAM](https://studiotvai.com/badge/minimax-m2.svg)](https://studiotvai.com/vram-requirements/minimax-m2)

Other models

Same family (large moe): MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Atria Dawn Preview · Intern S2 397B · DeepSeek-V3 · DeepSeek-V3.1 · Kimi-K2.7-Code · Kimi-K2.5 · Kimi-K2 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiMo-V2.5 · Ornith-1.0 397B

All models: Llama 3.1 8B · Mistral Small 3.2 24B · Qwen3 32B · Llama 3.3 70B · Llama 3.1 405B · Qwen3.5 4B · Qwen3.5 9B · Qwen3.8 27B · Qwen3.6 35B-A3B · Qwen3.5 122B-A10B · Qwen3.8-Flash-Next · LFM2.5 8B-A1B · LFM2 24B-A2B · Gemma 4 12B · Gemma 4 31B · Gemma 4 26B-A4B · gpt-oss 20B · gpt-oss 120B · Llama 4 Scout 17B-16E · Llama 4 Maverick 17B-128E · Kolibri-1 · Nemotron 3 Nano 30B-A3B · Nemotron 3 Super 120B-A12B · MiniMax M2.7 · DeepSeek R1 · Kimi K2.6 · GLM-5.3 · GLM-5.3-Flash · DeepSeek V4 Flash · DeepSeek V4 Pro · DeepSeek V4.1 Flash · MiMo-V2.6 Pro · MiMo-V2.6 Flash · Kimi K3 · Mistral Large 4 · Qwen3-Coder-Next 80B-A3B · Qwen3-Coder 30B-A3B · Qwen3-Coder 480B-A35B · DeepSeek V4 Flash Vision Exp · Qwen3.8 2.4T-A95B · Ornith 1.5 397B · Ornith 1.5 35B-A3B · Ornith 1.5 9B · Atria Dawn Preview · Intern S2 397B · Clef · Clef Flash · D1 3B · Humanizer · JEV 27B VL · Mellum2.1 12B-A2.5B · LightOnOCR-3 4B · Spark-X2.5 4B · Agnes-3.0-Qwen · Youtu-Parsing-Omni · Agnes-3.0-Flash · Mistral 7B-v0.3 · Mistral-Small-3.1 24B · Mistral-Nemo · Ministral 8B · DeepSeek-V3 · DeepSeek-R1-Qwen3 8B · DeepSeek-R1-Distill-Qwen 32B · DeepSeek-V3.1 · DeepSeek-R1-Distill-Qwen 14B · DeepSeek-R1-Distill-Qwen 7B · DeepSeek-R1-Distill-Llama 8B · Qwen3 4B · Qwen3 8B · Qwen2.5 7B · Qwen3 14B · Qwen2.5 3B · Qwen3.6 27B · Qwen3.5 27B · Qwen2.5-Coder 7B · Qwen2.5 32B · Qwen2.5-Coder 14B · Qwen3.5 35B-A3B · Qwen3 30B-A3B · Qwen2.5 14B · Qwen2.5-Coder 32B · Kimi-K2.7-Code · Kimi-K2.5 · Kimi-K2 · GLM-5.2 · GLM-5.1 · GLM-5 · MiniMax-M2.5 · MiMo-V2.5 · Ornith-1.0 397B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.