Can I Run GLM-4.7 / GLM-Z1 on 32 GB system RAM?

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

32 GB system RAM — what it gives a model

Usable memory for models	25.6 GB
Memory bandwidth	90 GB/s

GLM-4.7 / GLM-Z1 on 32 GB system RAM: memory by quantization

Quant	Memory needed	Fits 25.6 GB?	Max context	Est. speed	Download
F16	66.6 GB	✗ No	—	—	64 GB
Q8_0	36.6 GB	✗ No	—	—	34 GB
Q6_K	28.9 GB	✗ No	—	—	26.2 GB
Q5_K_M	25.3 GB	✓ Yes	8K	~2.8 tok/s	22.7 GB
Q4_K_M	22 GB	✓ Yes	16K	~3.3 tok/s	19.3 GB
Q3_K_M	16.3 GB	✓ Yes	32K	~4.6 tok/s	13.6 GB
Q2_K	13.2 GB	✓ Yes	32K	~5.8 tok/s	10.5 GB

Which GLM-4.7 / GLM-Z1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-Z1 32B (Reasoning)	22 GB	✓ Fits	~3.3 tok/s
GLM-4.7-Flash 30B-A3B	20.7 GB	✓ Fits	~23.6 tok/s
GLM-4.7 9B	7.4 GB	✓ Fits	~10.9 tok/s

What to watch out for

• Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
• 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.
• 25.6 GB of the 32 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

Can I run GLM-4.7 / GLM-Z1 on 32 GB system RAM?

Yes, comfortably — GLM-Z1 32B (Reasoning) at Q2_K needs about 13.2 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~12.4 GB spare and running at ~5.8 tok/s (estimated), with room for about 32,768 tokens of context.

Which quantization of GLM-4.7 / GLM-Z1 should I use on 32 GB system RAM?

Q2_K — it needs about 13.2 GB of the 25.6 GB available, downloads as roughly 10.5 GB, and runs at an estimated 5.8 tokens/sec with up to 32K of context.

What limits GLM-4.7 / GLM-Z1 on 32 GB system RAM?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

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

Other RAM Capacities

• GLM-4.7 / GLM-Z1 on 24 GB RAM
• GLM-4.7 / GLM-Z1 on 16 GB RAM
• GLM-4.7 / GLM-Z1 on 48 GB RAM
• GLM-4.7 / GLM-Z1 on 8 GB RAM

Other Models on 32 GB system RAM

• GLM-5 / GLM-5.1 on 32 GB system RAM
• GLM-6 on 32 GB system RAM
• GPT-OSS on 32 GB system RAM
• Granite 3.0 on 32 GB system RAM
• IBM Granite 4.0 on 32 GB system RAM

GLM-4.7 / GLM-Z1 on GPUs

• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5090
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5070
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060 Ti 8GB
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060

What This Model Is Good At

• Best local LLMs for reasoning
• Best local LLMs for research
• Best local LLMs for mathematics

Model & Tools

• GLM-4.7 / GLM-Z1 model page
• VRAM calculator
• Check your own hardware

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