Can I Run GLM-6 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated June 26, 2026

Yes, but it is tight

Yes, but it is tight — GLM-6 9B at Q8_0 needs about 11.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.4 GB before the runtime starts swapping. Expect ~25.1 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~25.1 tok/s

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RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	360 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

GLM-6 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	20 GB	✗ No	—	—	18 GB
Q8_0	11.6 GB	✓ Yes	8K	~25.1 tok/s	9.6 GB
Q6_K	9.4 GB	✓ Yes	16K	~31.4 tok/s	7.4 GB
Q5_K_M	8.4 GB	✓ Yes	16K	~35.6 tok/s	6.4 GB
Q4_K_M	7.4 GB	✓ Yes	32K	~40.6 tok/s	5.4 GB
Q3_K_M	5.8 GB	✓ Yes	32K	~53.5 tok/s	3.8 GB
Q2_K	5 GB	✓ Yes	32K	~64.8 tok/s	3 GB

Which GLM-6 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-6 355B-A32B	219.2 GB	✗ Too large	—
GLM-6 9B	7.4 GB	✓ Fits	~40.6 tok/s

What to watch out for

Only ~0.4 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
1 larger variant of GLM-6 does 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 3060 12 GB desktop limitations

The budget entry point to local AI: 12 GB runs 7–14B models well and nothing larger without offload.

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.
12 GB of VRAM on the NVIDIA GeForce RTX 3060 (12GB) at 360 GB/s.
32 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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