Can I Run GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Written by Jakub Rusinowski · Last updated May 1, 2026

Yes, but it is tight

Yes, but it is tight — GLM-5 9B at Q4_K_M needs about 7.4 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.6 GB before the runtime starts swapping. Expect ~29.7 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~29.7 tok/s

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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models	8 GB
Memory bandwidth	256 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes
Price	$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

Quant	Memory needed	Fits 8 GB?	Max context	Est. speed	Download
F16	20 GB	✗ No	—	—	18 GB
Q8_0	11.6 GB	✗ No	—	—	9.6 GB
Q6_K	9.4 GB	✗ No	—	—	7.4 GB
Q5_K_M	8.4 GB	✗ No	—	—	6.4 GB
Q4_K_M	7.4 GB	✓ Yes	8K	~29.7 tok/s	5.4 GB
Q3_K_M	5.8 GB	✓ Yes	16K	~39.4 tok/s	3.8 GB
Q2_K	5 GB	✓ Yes	16K	~48.1 tok/s	3 GB

Which GLM-5 / GLM-5.1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-5 744B	455.2 GB	✗ Too large	—
GLM-5.1 72B	46.7 GB	✗ Too large	—
GLM-5 32B	22 GB	✗ Too large	—
GLM-5 9B	7.4 GB	✓ Fits	~29.7 tok/s

What to watch out for

Only ~0.6 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
3 larger variants of GLM-5 / GLM-5.1 do not fit and would need CPU offload or different hardware.
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 4060 laptop limitations

8 GB VRAM limits you to 7–8B models at Q4 with a short context.
System RAM is usually upgradeable on this class of laptop even though VRAM is not.

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.
8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
16 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.

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