Can I Run GLM-5 / GLM-5.1 on 96 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年5月1日

Technically yes, but not recommended

It loads, but it is not worth running — GLM-5.1 72B at Q6_K fits in 96 GB system RAM's 76.8 GB, yet the memory bandwidth limits it to ~1.1 tok/s (estimated), well below usable interactive speed.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~1.1 tok/s

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96 GB system RAM — what it gives a model

Usable memory for models	76.8 GB
Memory bandwidth	90 GB/s

GLM-5 / GLM-5.1 on 96 GB system RAM: memory by quantization

Quant	Memory needed	Fits 76.8 GB?	Max context	Est. speed	Download
F16	147.2 GB	✗ No	—	—	144 GB
Q8_0	79.7 GB	✗ No	—	—	76.5 GB
Q6_K	62.3 GB	✓ Yes	32K	~1.1 tok/s	59 GB
Q5_K_M	54.2 GB	✓ Yes	64K	~1.3 tok/s	51 GB
Q4_K_M	46.7 GB	✓ Yes	64K	~1.5 tok/s	43.5 GB
Q3_K_M	33.9 GB	✓ Yes	64K	~2.1 tok/s	30.7 GB
Q2_K	26.9 GB	✓ Yes	64K	~2.7 tok/s	23.7 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	✓ Fits	~1.5 tok/s
GLM-5 32B	22 GB	✓ Fits	~3.3 tok/s
GLM-5 9B	7.4 GB	✓ Fits	~10.9 tok/s

What to watch out for

At ~1.1 tok/s this loads but is too slow for interactive use — expect roughly 55 seconds per 60 tokens.
1 larger variant of GLM-5 / GLM-5.1 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.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
76.8 GB of the 96 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
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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