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Can the NVIDIA GeForce RTX 5090 run Mistral Small 4 (119B-A6B)?

56 ptsUsable (C) · 56 of 100 points: Model quality 25.4/45, Memory fit 4.0/25, Speed 12.5/15, Memory use 15.0/15

Barely: most of the model spills into system RAM

Heavy offload. At Q2_K it needs about 50.1 GB of 32.0 GB usable, with an estimated speed of 27.8 tok/s.

Best quant
Q2_K
Generation speed
27.8 tok/s
Prompt processing
9,231 tok/s
First token (1K prompt)
0.3 s
Largest safe context
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Download Mistral Small 4 (119B-A6B)

Recommended for the NVIDIA GeForce RTX 5090: Q2_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K (UD-Q2_K_XL) file (opens in a new tab)

huggingface.co/unsloth/Mistral-Small-4-119B-2603-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q2_K_XL

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Small-4-119B-2603 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Mistral AI. The weights themselves download from Hugging Face.

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the C grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.25.4 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.5 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total56.9 / 10056 ptsUsable (C) · 56 of 100 points: Model quality 25.4/45, Memory fit 4.0/25, Speed 12.5/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 5090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 50.1 GB 32.0 GB 27.8 tok/s Heavy offload 56 ptsUsable (C) · 56 of 100 points: Model quality 25.4/45, Memory fit 4.0/25, Speed 12.5/15, Memory use 15.0/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 60.9 GB 32.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q4_K_M 75.2 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q5_K_M 87.2 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q6_K 100 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q8_0 129 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 241 GB 32.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

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.