Can I Run GLM-5 / GLM-5.1 on 24 GB system RAM?

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

No — GLM-5 / GLM-5.1 does not fit in 24 GB system RAM. Even at Q2_K it needs about 250.6 GB against 19.2 GB usable, a shortfall of roughly 231.4 GB.

Confidence: medium

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24 GB system RAM — what it gives a model

Usable memory for models	19.2 GB
Memory bandwidth	90 GB/s

Quant	Memory needed	Fits 19.2 GB?	Max context	Est. speed	Download
F16	1494 GB	✗ No	—	—	1488 GB
Q8_0	796.5 GB	✗ No	—	—	790.5 GB
Q6_K	616.1 GB	✗ No	—	—	610.1 GB
Q5_K_M	533.3 GB	✗ No	—	—	527.3 GB
Q4_K_M	455.2 GB	✗ No	—	—	449.2 GB
Q3_K_M	323.2 GB	✗ No	—	—	317.1 GB
Q2_K	250.6 GB	✗ No	—	—	244.6 GB

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.

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

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
19.2 GB of the 24 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.

No — GLM-5 / GLM-5.1 does not fit in 24 GB system RAM. Even at Q2_K it needs about 250.6 GB against 19.2 GB usable, a shortfall of roughly 231.4 GB.

Memory. 24 GB system RAM provides about 19.2 GB usable, and the smallest quantization of this model needs 250.6 GB.