LLMs ·

Pixtral Large 124B (2411)

Parameters
124B
Architecture
Dense
Max context
128K tokens
License
mrl
Released
2024-11

How to run it

Original weights · Hugging Face

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

huggingface.co/mistralai/Pixtral-Large-Instruct-2411 (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

Apps that run Pixtral Large 124B (2411)

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 mistralai/Pixtral-Large-Instruct-2411
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path mistralai/Pixtral-Large-Instruct-2411
Docker Model Runner CLI + API Original weights macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/mistralai/Pixtral-Large-Instruct-2411
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.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 49.6 GB 53.2 GB 62.0 GB All files (opens in a new tab)
Q3_K_M Low 60.8 GB 64.3 GB 73.2 GB All files (opens in a new tab)
Q4_K_M Good 75.6 GB 79.2 GB 88.1 GB All files (opens in a new tab)
Q5_K_M High 88.0 GB 91.6 GB 100 GB All files (opens in a new tab)
Q6_K High 102 GB 105 GB 114 GB All files (opens in a new tab)
Q8_0 Very high 131 GB 135 GB 144 GB All files (opens in a new tab)
F16 Full 248 GB 252 GB 260 GB Original weights (opens in a new tab)

Best hardware for Pixtral Large 124B (2411)

The least expensive GPU in our catalog that runs it without offloading is the NVIDIA RTX PRO 6000 Blackwell (launch price $8,565) at Q4_K_M.

Quality Hardware Quant Memory Speed Runs
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Workstation EditionNVIDIA RTX PRO 6000 Blackwell Q4_K_M 84.0 GB / 96.0 GB 17.7 tok/s Runs welltight fit
54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2023) (M2 Max or M2 Ultra)Apple M2 Ultra (192 GB) Q2_K 64.7 GB / 144 GB 11.2 tok/s Runs well
54Fair quality (C) · 54 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. Apple Mac Studio (2025) (M4 Max or M3 Ultra)Apple M3 Ultra (256 GB) Q2_K 68.5 GB / 192 GB 11.5 tok/s Runs well
54Fair quality (C) · 54 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 (512 GB) Q2_K 83.9 GB / 384 GB 11.5 tok/s Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200NVIDIA B200 Q8_0 144 GB / 180 GB 40.3 tok/s Runs great
75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXMNVIDIA H200 141GB SXM Q5_K_M 98.6 GB / 141 GB 39.3 tok/s Runs great
75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 NVLNVIDIA H200 NVL Q5_K_M 98.6 GB / 141 GB 39.3 tok/s Runs great
74Great quality (A) · 74 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. NVIDIA GH200 Grace Hopper (96 GB GPU)NVIDIA GH200 Grace Hopper (96 GB GPU) Q4_K_M 84.0 GB / 96.0 GB 39.6 tok/s Runs welltight fit
74Great quality (A) · 74 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. NVIDIA H20 Q4_K_M 84.0 GB / 96.0 GB 39.6 tok/s Runs welltight fit
74Great quality (A) · 74 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. NVIDIA H100 NVLNVIDIA H100 NVL Q4_K_M 83.9 GB / 94.0 GB 38.6 tok/s Runs welltight fit

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.

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