Mistral Large 2 (123B) on AMD Radeon RX 9060 XT 8GB

Can the AMD Radeon RX 9060 XT 8GB run Mistral Large 2 (123B)?

7 pts

No, it doesn’t fit

No. Mistral Large 2 (123B) needs about 53.2 GB even at Q2_K; the AMD Radeon RX 9060 XT 8GB has 8.0 GB usable.

Download Mistral Large 2 (123B)

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

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run mistral-large:123b-instruct-2407-q2_K

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

Download the Q2_K file (opens in a new tab)

huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Large-Instruct-2407 (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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.	7.4 / 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	7.4 / 100	7 ptsWon't run (F) · 7 of 100 points: Model quality 7.4/45, 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 AMD Radeon RX 9060 XT 8GB

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	53.2 GB	8.0 GB	—	Won't run	7 ptsWon't run (F) · 7 of 100 points: Model quality 7.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q3_K_M`	64.2 GB	8.0 GB	—	Won't run	8 ptsWon't run (F) · 8 of 100 points: Model quality 8.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q4_K_M`	79.0 GB	8.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q5_K_M`	91.3 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)
`Q6_K`	105 GB	8.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q8_0`	134 GB	8.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`F16`	250 GB	8.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	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

82 ptsGreat (A) · 82 of 100 points: Model quality 33.3/45, Memory fit 24.0/25, Speed 10.0/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 17.9 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 15.0/15 Apple M2 Ultra (192 GB) Q6_K · 4.8 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 14.4/15 Apple M3 Ultra (256 GB) Q6_K · 4.9 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 33.3/45, Memory fit 24.0/25, Speed 5.1/15, Memory use 15.0/15 Apple M4 Max (128 GB) Q4_K_M · 4.7 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 3.9/15, Memory use 15.0/15 NVIDIA GB10 (128 GB) Q4_K_M · 2.7 tok/s
75 ptsGreat (A) · 75 of 100 points: Model quality 33.3/45, Memory fit 24.0/25, Speed 3.5/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) Q4_K_M · 2.2 tok/s

Models that do run on the AMD Radeon RX 9060 XT 8GB

86 ptsExcellent (S) · 86 of 100 points: Model quality 31.6/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 15.0/15 TMax 9B Q4_K_M · 37.8 tok/s
85 ptsExcellent (S) · 85 of 100 points: Model quality 32.9/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 Ornith 1.5 9B Q4_K_M · 35.1 tok/s
85 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 Qwen3.5 9B Q4_K_M · 35.1 tok/s
85 ptsExcellent (S) · 85 of 100 points: Model quality 32.1/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 Ornith 1.0 9B Q4_K_M · 35.1 tok/s
84 ptsGreat (A) · 84 of 100 points: Model quality 31.4/45, Memory fit 24.0/25, Speed 14.3/15, Memory use 15.0/15 Gemma 4 12B Q3_K_M · 36.6 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.