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Can the AMD Radeon PRO W7800 32GB run Qwen3.8 27B?

91 ptsExcellent (S) · 91 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 14.2/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 19.7 GB of 32.0 GB usable, with an estimated speed of 21.9 tok/s.

Best quant
Q4_K_M
Generation speed
21.9 tok/s
Prompt processing
2,018 tok/s
First token (1K prompt)
0.5 s
Largest safe context
191K

Download Qwen3.8 27B

Recommended for the AMD Radeon PRO W7800 32GB: 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 qwen3.8:27b-q4_K_M

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 Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Qwen3.8-27B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.8-27B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/QwenLM/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

How the S 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.40.9 / 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.11.1 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.14.2 / 15
Total91.2 / 10091 ptsExcellent (S) · 91 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 14.2/15

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

Every quantization on the AMD Radeon PRO W7800 32GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 13.8 GB 32.0 GB 36.0 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 30.1/45, Memory fit 25.0/25, Speed 14.2/15, Memory use 10.0/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 16.3 GB 32.0 GB 28.3 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 36.1/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 11.8/15 GGUF (UD-Q3_K_XL) (opens in a new tab)
Q4_K_M 19.7 GB 32.0 GB 21.9 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 14.2/15 GGUF (opens in a new tab)
Q5_K_M 22.5 GB 32.0 GB 18.1 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 41.7/45, Memory fit 25.0/25, Speed 10.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 25.5 GB 32.0 GB 15.2 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 42.6/45, Memory fit 25.0/25, Speed 9.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 32.2 GB 32.0 GB 10.7 tok/s Runs with offload 75 ptsGreat (A) · 75 of 100 points: Model quality 43.0/45, Memory fit 10.0/25, Speed 7.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 58.3 GB 32.0 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.9/45, 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.