Mistral Large 3 (675B) on NVIDIA GeForce RTX 5060 Ti 8GB

Can the NVIDIA GeForce RTX 5060 Ti 8GB run Mistral Large 3 (675B)?

8 pts

No, it doesn’t fit

No. Mistral Large 3 (675B) needs about 272 GB even at Q2_K; the NVIDIA GeForce RTX 5060 Ti 8GB has 8.0 GB usable.

Download Mistral Large 3 (675B)

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Ollama

Ollama offers this only as a cloud model (it runs on Ollama’s servers, not your machine). To run it locally, use the GGUF files below.

Quantized files · GGUF

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

Download the Q2_K files (opens in a new tab)

huggingface.co/unsloth/Mistral-Large-3-675B-Instruct-2512-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Large-3-675B-Instruct-2512-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512 (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 F 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.	8.5 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Total	8.5 / 100	8 ptsWon't run (F) · 8 of 100 points: Model quality 8.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

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

Every quantization on the NVIDIA GeForce RTX 5060 Ti 8GB

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	272 GB	8.0 GB	—	Won't run	8 ptsWon't run (F) · 8 of 100 points: Model quality 8.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q3_K_M`	332 GB	8.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q4_K_M`	413 GB	8.0 GB	—	Won't run	11 ptsWon't run (F) · 11 of 100 points: Model quality 11.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q5_K_M`	481 GB	8.0 GB	—	Won't run	11 ptsWon't run (F) · 11 of 100 points: Model quality 11.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q6_K`	555 GB	8.0 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q8_0`	717 GB	8.0 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`F16`	1,352 GB	8.0 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.2/45, 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.

Hardware that can run it

81 ptsGreat (A) · 81 of 100 points: Model quality 34.0/45, Memory fit 24.0/25, Speed 8.8/15, Memory use 15.0/15 Apple M3 Ultra (512 GB) Q3_K_M · 13.6 tok/s

Models that do run on the NVIDIA GeForce RTX 5060 Ti 8GB

86 ptsExcellent (S) · 86 of 100 points: Model quality 32.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Ornith 1.5 9B Q4_K_M · 57.0 tok/s
86 ptsExcellent (S) · 86 of 100 points: Model quality 31.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 TMax 9B Q4_K_M · 61.4 tok/s
86 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Qwen3.5 9B Q4_K_M · 57.0 tok/s
86 ptsExcellent (S) · 86 of 100 points: Model quality 32.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Ornith 1.0 9B Q4_K_M · 57.0 tok/s
85 ptsExcellent (S) · 85 of 100 points: Model quality 31.4/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Gemma 4 12B Q3_K_M · 59.4 tok/s

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.