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Can the NVIDIA RTX PRO 5000 Blackwell 72GB run GLM-4.7 Flash (30B-A3B)?

91 ptsExcellent (S) · 91 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15

Yes, it runs well

Runs well. At Q8_0 it needs about 37.7 GB of 72.0 GB usable, with an estimated speed of 140 tok/s.

Best quant
Q8_0
Generation speed
140 tok/s
Prompt processing
15,600 tok/s
First token (1K prompt)
0.3 s
Largest safe context
198K

Download GLM-4.7 Flash (30B-A3B)

Recommended for the NVIDIA RTX PRO 5000 Blackwell 72GB: Q8_0. 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:q8_0

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 Q8_0 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:Q8_0

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 hours 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.39.6 / 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.12.1 / 15
Total91.7 / 10091 ptsExcellent (S) · 91 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15

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

Every quantization on the NVIDIA RTX PRO 5000 Blackwell 72GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 17.1 GB 72.0 GB 452 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 27.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF (opens in a new tab)
Q3_K_M 19.9 GB 72.0 GB 355 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q4_K_M 23.7 GB 72.0 GB 275 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 37.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.6/15 GGUF (opens in a new tab)
Q5_K_M 26.8 GB 72.0 GB 227 tok/s Runs well 87 ptsExcellent (S) · 87 of 100 points: Model quality 38.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.6/15 GGUF (opens in a new tab)
Q6_K 30.2 GB 72.0 GB 191 tok/s Runs well 88 ptsExcellent (S) · 88 of 100 points: Model quality 39.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.7/15 GGUF (opens in a new tab)
Q8_0 37.7 GB 72.0 GB 140 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15 GGUF (opens in a new tab)
F16 67.1 GB 72.0 GB 64.5 tok/s Tight fit 93 ptsExcellent (S) · 93 of 100 points: Model quality 39.6/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 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.