Can I Run GLM-4.7 / GLM-Z1 on 16 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Yes, but it is tight

Yes, but it is tight — GLM-4.7 9B at Q8_0 needs about 11.6 GB of the 12.8 GB usable on 16 GB system RAM, leaving only ~1.2 GB before the runtime starts swapping. Expect ~6.5 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~6.5 tok/s

Buy This HardwareIntel Arc B580 12GB — 12 GB VRAM · 190 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

16 GB system RAM — what it gives a model

Usable memory for models	12.8 GB
Memory bandwidth	90 GB/s

GLM-4.7 / GLM-Z1 on 16 GB system RAM: memory by quantization

Quant	Memory needed	Fits 12.8 GB?	Max context	Est. speed	Download
F16	20 GB	✗ No	—	—	18 GB
Q8_0	11.6 GB	✓ Yes	16K	~6.5 tok/s	9.6 GB
Q6_K	9.4 GB	✓ Yes	16K	~8.3 tok/s	7.4 GB
Q5_K_M	8.4 GB	✓ Yes	32K	~9.4 tok/s	6.4 GB
Q4_K_M	7.4 GB	✓ Yes	32K	~10.9 tok/s	5.4 GB
Q3_K_M	5.8 GB	✓ Yes	32K	~14.6 tok/s	3.8 GB
Q2_K	5 GB	✓ Yes	32K	~18.1 tok/s	3 GB

Which GLM-4.7 / GLM-Z1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-Z1 32B (Reasoning)	22 GB	✗ Too large	—
GLM-4.7-Flash 30B-A3B	20.7 GB	✗ Too large	—
GLM-4.7 9B	7.4 GB	✓ Fits	~10.9 tok/s

What to watch out for

Only ~1.2 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
2 larger variants of GLM-4.7 / GLM-Z1 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.
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.
12.8 GB of the 16 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.

FAQ