Can I Run GLM-4.7 / GLM-Z1 on 128 GB system RAM?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes — comfortably
Yes, comfortably — GLM-Z1 32B (Reasoning) at Q2_K needs about 13.2 GB of the 102.4 GB usable on 128 GB system RAM, leaving ~89.2 GB spare and running at ~5.8 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~5.8 tok/s
128 GB system RAM — what it gives a model
| Usable memory for models | 102.4 GB |
| Memory bandwidth | 90 GB/s |
GLM-4.7 / GLM-Z1 on 128 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 102.4 GB? | Max context | Est. speed | Download |
|---|
| F16 | 66.6 GB | ✓ Yes | 64K | ~1 tok/s | 64 GB |
| Q8_0 | 36.6 GB | ✓ Yes | 64K | ~1.9 tok/s | 34 GB |
| Q6_K | 28.9 GB | ✓ Yes | 64K | ~2.5 tok/s | 26.2 GB |
| Q5_K_M | 25.3 GB | ✓ Yes | 64K | ~2.8 tok/s | 22.7 GB |
| Q4_K_M | 22 GB | ✓ Yes | 64K | ~3.3 tok/s | 19.3 GB |
| Q3_K_M | 16.3 GB | ✓ Yes | 64K | ~4.6 tok/s | 13.6 GB |
| Q2_K | 13.2 GB | ✓ Yes | 64K | ~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.
- 102.4 GB of the 128 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 128 GB system RAM?
Yes, comfortably — GLM-Z1 32B (Reasoning) at Q2_K needs about 13.2 GB of the 102.4 GB usable on 128 GB system RAM, leaving ~89.2 GB spare and running at ~5.8 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of GLM-4.7 / GLM-Z1 should I use on 128 GB system RAM?
Q2_K — it needs about 13.2 GB of the 102.4 GB available, downloads as roughly 10.5 GB, and runs at an estimated 5.8 tokens/sec with up to 64K of context.
What limits GLM-4.7 / GLM-Z1 on 128 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
Other Models on 128 GB system RAM
GLM-4.7 / GLM-Z1 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | GLM-4.7 / GLM-Z1 model page | Check your hardware