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Can the NVIDIA RTX A5500 run GLM-4.7 Flash (30B-A3B)?

91 ptsExcellent (S) · 91 of 100 points: Model quality 37.6/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 21.3 GB of 24.0 GB usable, with an estimated speed of 157 tok/s.

Best quant
Q4_K_M
Generation speed
157 tok/s
Prompt processing
8,184 tok/s
First token (1K prompt)
0.3 s
Largest safe context
56K

Download GLM-4.7 Flash (30B-A3B)

Recommended for the NVIDIA RTX A5500: 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 glm-4.7-flash: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/GLM-4.7-Flash-GGUF (opens in a new tab)

ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:Q4_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 6 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.37.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total91.6 / 10091 ptsExcellent (S) · 91 of 100 points: Model quality 37.6/45, Memory fit 24.0/25, 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 NVIDIA RTX A5500

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 14.7 GB 24.0 GB 258 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 27.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.2/15 GGUF (opens in a new tab)
Q3_K_M 17.5 GB 24.0 GB 203 tok/s Runs well 88 ptsExcellent (S) · 88 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 21.3 GB 24.0 GB 157 tok/s Tight fit 91 ptsExcellent (S) · 91 of 100 points: Model quality 37.6/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 24.4 GB 24.0 GB 109 tok/s Runs with offload 78 ptsGreat (A) · 78 of 100 points: Model quality 38.4/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 27.8 GB 24.0 GB 42.3 tok/s Runs with offload 79 ptsGreat (A) · 79 of 100 points: Model quality 39.2/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 35.3 GB 24.0 GB 17.4 tok/s Heavy offload 68 ptsGood (B) · 68 of 100 points: Model quality 39.6/45, Memory fit 4.0/25, Speed 9.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 64.7 GB 24.0 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.