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Can the AMD Radeon AI PRO R9700 run GLM-4.7 Flash (30B-A3B)?

93 ptsExcellent (S) · 93 of 100 points: Model quality 38.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 24.8 GB of 32.0 GB usable, with an estimated speed of 93.1 tok/s.

Best quant
Q5_K_M
Generation speed
93.1 tok/s
Prompt processing
19,778 tok/s
First token (1K prompt)
0.3 s
Largest safe context
137K

Download GLM-4.7 Flash (30B-A3B)

Recommended for the AMD Radeon AI PRO R9700: Q5_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 glm-4.7-flash

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 Q5_K_M file (opens in a new tab)

huggingface.co/unsloth/GLM-4.7-Flash-GGUF (opens in a new tab)

ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/zai-org/GLM-4.7-Flash (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/zai-org/GLM-4.5 (opens in a new tab)

git clone https://github.com/zai-org/GLM-4.5

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

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.38.4 / 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
Total93.4 / 10093 ptsExcellent (S) · 93 of 100 points: Model quality 38.4/45, 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 AI PRO R9700

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 15.1 GB 32.0 GB 185 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 27.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF (opens in a new tab)
Q3_K_M 17.9 GB 32.0 GB 146 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 GGUF (opens in a new tab)
Q4_K_M 21.7 GB 32.0 GB 113 tok/s Runs well 92 ptsExcellent (S) · 92 of 100 points: Model quality 37.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 24.8 GB 32.0 GB 93.1 tok/s Runs well 93 ptsExcellent (S) · 93 of 100 points: Model quality 38.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 28.2 GB 32.0 GB 78.2 tok/s Tight fit 93 ptsExcellent (S) · 93 of 100 points: Model quality 39.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 35.7 GB 32.0 GB 31.5 tok/s Runs with offload 77 ptsGreat (A) · 77 of 100 points: Model quality 39.6/45, Memory fit 10.0/25, Speed 13.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 65.1 GB 32.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.9/45, 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.