LLMs ·

MiniMax-M1 80K

Parameters
456B (45.9B active)
Architecture
Mixture of experts
Max context
976K tokens
License
apache-2.0
Released
2025-06

How to run it

Original weights · Hugging Face

Full-precision files from MiniMax, for fine-tuning, vLLM, Transformers.

huggingface.co/MiniMaxAI/MiniMax-M1-80k (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

Apps that run MiniMax-M1 80K

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Windows PC, AMD Radeon

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App Type Opens it as Systems Engine Start
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve MiniMaxAI/MiniMax-M1-80k
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path MiniMaxAI/MiniMax-M1-80k
Docker Model Runner CLI + API Original weights macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/MiniMaxAI/MiniMax-M1-80k
Text Generation WebUI Desktop app Original weights macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

KV cache: hybrid linear attention: only the softmax-attention layers (1 in 8) keep a KV cache.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 182 GB 183 GB 184 GB All files (opens in a new tab)
Q3_K_M Low 223 GB 224 GB 225 GB All files (opens in a new tab)
Q4_K_M Good 278 GB 279 GB 280 GB All files (opens in a new tab)
Q5_K_M High 324 GB 325 GB 326 GB All files (opens in a new tab)
Q6_K High 374 GB 375 GB 376 GB All files (opens in a new tab)
Q8_0 Very high 483 GB 484 GB 485 GB All files (opens in a new tab)
F16 Full 912 GB 913 GB 914 GB Original weights (opens in a new tab)

Best hardware for MiniMax-M1 80K

Quality Hardware Quant Memory Speed Runs
78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2025) (M4 Max or M3 Ultra)Apple M3 Ultra (512 GB) Q4_K_M 310 GB / 384 GB 17.7 tok/s Runs well
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2025) (M4 Max or M3 Ultra)Apple M3 Ultra (256 GB) Q2_K 199 GB / 192 GB 24.0 tok/s Runs wellover GPU memory limit
78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (M5 Max or M5 Ultra)Apple M5 Ultra (512 GB) Q4_K_M 310 GB / 384 GB 25.3 tok/s Runs well
69Good quality (B) · 69 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI350XAMD Instinct MI350X Q3_K_M 239 GB / 288 GB 134 tok/s Runs great
69Good quality (B) · 69 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325XAMD Instinct MI325X Q3_K_M 237 GB / 256 GB 113 tok/s Runs welltight fit
69Good quality (B) · 69 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Dell Pro Max with GB300NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip Q3_K_M 237 GB / 252 GB 137 tok/s Runs welltight fit
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI300XAMD Instinct MI300X Q2_K 193 GB / 192 GB 101 tok/s Runs welloffloads to RAM
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200NVIDIA B200 Q2_K 192 GB / 180 GB 34.6 tok/s Runs welloffloads to RAM
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (M5 Max or M5 Ultra)Apple M5 Ultra (256 GB) Q2_K 199 GB / 192 GB 33.8 tok/s Runs wellover GPU memory limit
78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. QNAP QAI-h1290FXAMD EPYC 7002 / 7003 series NAS (512 GB) Q4_K_M 317 GB / 471 GB 3.0 tok/s Slow

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

Was this page helpful?