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Can the AMD Radeon RX 9060 XT 8GB run LLaVA 1.6 Mistral 7B?

66 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 6.7 GB of 8.0 GB usable, with an estimated speed of 44.7 tok/s.

Best quant
Q4_K_M
Generation speed
44.7 tok/s
Prompt processing
4,193 tok/s
First token (1K prompt)
0.3 s
Largest safe context
17K

Download LLaVA 1.6 Mistral 7B

Recommended for the AMD Radeon RX 9060 XT 8GB: Q4_K_M. 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 llava:7b-v1.6-mistral-q4_K_M

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

Original weights · Hugging Face

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

huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf (opens in a new tab)

No sign-in needed.

How the B 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.12.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 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
Total67.0 / 10066 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, 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 AMD Radeon RX 9060 XT 8GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.1 GB 8.0 GB 73.4 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 8.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15
Q3_K_M 5.8 GB 8.0 GB 57.8 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 10.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15
Q4_K_M 6.7 GB 8.0 GB 44.7 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15
Q5_K_M 7.4 GB 8.0 GB 36.9 tok/s Tight fit 65 ptsGood (B) · 65 of 100 points: Model quality 12.2/45, Memory fit 24.0/25, Speed 14.4/15, Memory use 15.0/15
Q6_K 8.3 GB 8.0 GB 27.2 tok/s Runs with offload 49 ptsUsable (C) · 49 of 100 points: Model quality 12.5/45, Memory fit 10.0/25, Speed 12.4/15, Memory use 15.0/15
Q8_0 10.1 GB 8.0 GB 12.5 tok/s Runs with offload 45 ptsUsable (C) · 45 of 100 points: Model quality 12.6/45, Memory fit 10.0/25, Speed 8.4/15, Memory use 15.0/15
F16 17.2 GB 8.0 GB 3.6 tok/s Heavy offload 36 ptsPoor (D) · 36 of 100 points: Model quality 12.6/45, Memory fit 4.0/25, Speed 4.5/15, Memory use 15.0/15 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.