GLM-5 744B — VRAM, Speed & Local Setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 lutego 2026

Model library → GLM-5 / GLM-5.1 → GLM-5 744B

Frontier MoE model with 744B total and 40B active parameters. Scores 77.8% on SWE-bench Verified and holds the highest open-source Chatbot Arena Elo at 1451. Trained on Huawei Ascend hardware — no NVIDIA dependency. Requires ~400+ GB VRAM at Q4; accessible via API at ~$0.80/1M input tokens.

GLM-5 744B needs about 450 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.

Specifications

Parameters	744 Billion (40B active)
Context window	200,000
Architecture	Mixture-of-Experts + DeepSeek Sparse Attention
Provider	Zhipu AI (Z.ai)
Licence	MIT
Specified at	Q4_K_M
System RAM	1024 GB
Record updated	2026-02-15

Licence

MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

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	244.6 GB	245.4 GB	—	Won't fit
Q3_K_M	317.1 GB	317.9 GB	—	Won't fit
Q4_K_M	449.2 GB	450.0 GB	—	Won't fit
Q5_K_M	527.3 GB	528.1 GB	—	Won't fit
Q6_K	610.1 GB	610.9 GB	—	Won't fit
Q8_0	790.5 GB	791.3 GB	—	Won't fit
F16	1488.0 GB	1488.8 GB	—	Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the GLM-5 744B VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs GLM-5 744B is the Apple M3 Ultra (512 GB).

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Apple Mac Studio M3 Ultra

512 GB VRAM · 60 W board power

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

How to Run GLM-5 744B

Install Ollama, then run:

ollama run glm-5

Weights on Hugging Face: THUDM/GLM-5.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

Benchmark	Score	Provenance
SWE-bench Verified	77.8 / 100 %	reported

Best for: software engineering, coding, enterprise, cloud api.

Can I Run GLM-5 744B on My GPU?

Other GLM-5 / GLM-5.1 Sizes

GLM-5 9B — about 6 GB VRAM at Q4_K_M
GLM-5 32B — about 20 GB VRAM at Q4_K_M
GLM-5.1 72B — about 44 GB VRAM at Q4_K_M

GLM-5 744B — Frequently Asked Questions

How much VRAM does GLM-5 744B need?

About 450 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does GLM-5 744B run on an RTX 4090 (24 GB)?

No. GLM-5 744B needs about 450 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run GLM-5 744B locally?

Install Ollama and run `ollama run glm-5`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does GLM-5 / GLM-5.1 come in?

GLM-5 9B (6 GB), GLM-5 32B (20 GB), GLM-5.1 72B (44 GB), GLM-5 744B (450 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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