Written by Jakub Rusinowski · Last updated April 10, 2026
Model library → GLM-5 / GLM-5.1 → GLM-5 32B
Mid-range GLM-5 with excellent balance of capability and resource requirements. Strong at agentic reasoning chains and long-context document processing. MIT license allows unrestricted commercial deployment.
GLM-5 32B needs about 20 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.
| Parameters | 32 Billion |
| Context window | 128,000 |
| Architecture | GLM (General Language Model) |
| Provider | Zhipu AI (Z.ai) |
| Licence | MIT |
| Specified at | Q4_K_M |
| System RAM | 32 GB |
| Record updated | 2026-04-10 |
MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.
Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.
| Quant | Weights | VRAM needed | Est. speed | Fit on 24 GB |
|---|---|---|---|---|
| Q2_K | 10.5 GB | 11.3 GB | ~58 tok/s (est.) | Fits comfortably |
| Q3_K_M | 13.6 GB | 14.4 GB | ~46 tok/s (est.) | Fits comfortably |
| Q4_K_M | 19.3 GB | 20.1 GB | ~34 tok/s (est.) | Fits comfortably |
| Q5_K_M | 22.7 GB | 23.5 GB | ~30 tok/s (est.) | Tight fit |
| Q6_K | 26.2 GB | 27.0 GB | ~4 tok/s (est.) | Offloads to system RAM (slow) |
| Q8_0 | 34.0 GB | 34.8 GB | ~3 tok/s (est.) | Offloads to system RAM (slow) |
| F16 | 64.0 GB | 64.8 GB | — | Won't fit |
Want the memory numbers alone, at every quantization level and your own context length? Use the GLM-5 32B VRAM calculator.
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The cheapest catalogued GPU that runs GLM-5 32B is the AMD Radeon RX 7900 XT (20 GB).
Install Ollama, then run:
ollama run hf.co/THUDM/GLM-5-32B-Chat-Q4_K_MWeights on Hugging Face: THUDM/GLM-5-32B-Chat.
Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.
| Benchmark | Score | Provenance |
|---|---|---|
| MMLU | 86.4 / 100 % | reported |
| HumanEval | 88.7 / 100 % | reported |
Best for: agentic, reasoning, document processing, coding, enterprise.
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