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Can the AMD Radeon RX 9060 XT 8GB run Qwen3-VL 32B Instruct?

53 ptsUsable (C) · 53 of 100 points: Model quality 29.5/45, Memory fit 4.0/25, Speed 4.8/15, Memory use 15.0/15

Barely: most of the model spills into system RAM

Heavy offload. At Q3_K_M it needs about 19.5 GB of 8.0 GB usable, with an estimated speed of 4.1 tok/s.

Best quant
Q3_K_M
Generation speed
4.1 tok/s
Prompt processing
483 tok/s
First token (1K prompt)
2.1 s
Largest safe context
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Download Qwen3-VL 32B Instruct

Quickest · Ollama

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

ollama run qwen3-vl:32b

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.

huggingface.co/Qwen/Qwen3-VL-32B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen3-VL-32B-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-VL-32B-Instruct (opens in a new tab)

No sign-in needed.

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.29.5 / 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.4.8 / 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
Total53.3 / 10053 ptsUsable (C) · 53 of 100 points: Model quality 29.5/45, Memory fit 4.0/25, Speed 4.8/15, 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 16.5 GB 8.0 GB 5.6 tok/s Heavy offload 49 ptsUsable (C) · 49 of 100 points: Model quality 24.6/45, Memory fit 4.0/25, Speed 5.6/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 19.5 GB 8.0 GB 4.1 tok/s Heavy offload 53 ptsUsable (C) · 53 of 100 points: Model quality 29.5/45, Memory fit 4.0/25, Speed 4.8/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 23.5 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 (opens in a new tab)
Q5_K_M 26.8 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 files (opens in a new tab)
Q6_K 30.5 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 files (opens in a new tab)
Q8_0 38.5 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 (opens in a new tab)
F16 69.9 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 · part 1 of 2 (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.