Can I Run GLM-4.7 / GLM-Z1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

Written by Jakub Rusinowski · Last updated August 15, 2026

RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	24 GB
Memory bandwidth	1008 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

GLM-4.7 / GLM-Z1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization

Quant	Memory needed	Fits 24 GB?	Max context	Est. speed	Download
F16	66.6 GB	✗ No	—	—	64 GB
Q8_0	36.6 GB	✗ No	—	—	34 GB
Q6_K	28.9 GB	✗ No	—	—	26.2 GB
Q5_K_M	25.3 GB	✗ No	—	—	22.7 GB
Q4_K_M	22 GB	✓ Yes	16K	~34.5 tok/s	19.3 GB
Q3_K_M	16.3 GB	✓ Yes	32K	~46.5 tok/s	13.6 GB
Q2_K	13.2 GB	✓ Yes	32K	~57.5 tok/s	10.5 GB

Which GLM-4.7 / GLM-Z1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-Z1 32B (Reasoning)	22 GB	✓ Fits	~34.5 tok/s
GLM-4.7-Flash 30B-A3B	20.7 GB	✓ Fits	~171.9 tok/s
GLM-4.7 9B	7.4 GB	✓ Fits	~97.7 tok/s

What to watch out for

• This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.

RTX 4090 desktop limitations

• 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second card.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

• Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
• 24 GB of VRAM on the NVIDIA GeForce RTX 4090 at 1008 GB/s.
• 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
• Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
• KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

FAQ

Can I run GLM-4.7 / GLM-Z1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

Yes, but it is tight — GLM-Z1 32B (Reasoning) at Q4_K_M needs about 22 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~2 GB before the runtime starts swapping. Expect ~34.5 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of GLM-4.7 / GLM-Z1 should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

Q4_K_M — it needs about 22 GB of the 24 GB available, downloads as roughly 19.3 GB, and runs at an estimated 34.5 tokens/sec with up to 16K of context.

What limits GLM-4.7 / GLM-Z1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

Other Computers

• GLM-4.7 / GLM-Z1 on RTX 5090 desktop
• GLM-4.7 / GLM-Z1 on Mac Studio M3 Ultra 256 GB
• GLM-4.7 / GLM-Z1 on RTX 3090 desktop
• GLM-4.7 / GLM-Z1 on Mac Studio M4 Max 64 GB

Other Models on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)

• GLM-5 / GLM-5.1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
• GLM-6 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
• GPT-OSS on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
• Granite 3.0 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
• IBM Granite 4.0 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)

GLM-4.7 / GLM-Z1 on GPUs

• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5090
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5070
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060 Ti 8GB
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060

What This Model Is Good At

• Best local LLMs for reasoning
• Best local LLMs for research
• Best local LLMs for mathematics

Model & Tools

• GLM-4.7 / GLM-Z1 model page
• VRAM calculator
• Check your own hardware

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