Can I Run GLM-5 / GLM-5.1 on 8 GB system RAM?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 maja 2026
No
No — GLM-5 / GLM-5.1 does not fit in 8 GB system RAM. Even at Q2_K it needs about 250.6 GB against 6.4 GB usable, a shortfall of roughly 244.2 GB.
Confidence: medium
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8 GB system RAM — what it gives a model
| Usable memory for models | 6.4 GB |
| Memory bandwidth | 90 GB/s |
GLM-5 / GLM-5.1 on 8 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 6.4 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 |
What to watch out for
- 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.
- 6.4 GB of the 8 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-5 / GLM-5.1 on 8 GB system RAM?
No — GLM-5 / GLM-5.1 does not fit in 8 GB system RAM. Even at Q2_K it needs about 250.6 GB against 6.4 GB usable, a shortfall of roughly 244.2 GB.
What limits GLM-5 / GLM-5.1 on 8 GB system RAM?
Memory. 8 GB system RAM provides about 6.4 GB usable, and the smallest quantization of this model needs 250.6 GB.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
- GLM-5 / GLM-5.1 on 16 GB RAM
- GLM-5 / GLM-5.1 on 24 GB RAM
- GLM-5 / GLM-5.1 on 32 GB RAM
- GLM-5 / GLM-5.1 on 48 GB RAM
Other Models on 8 GB system RAM
- Granite 3.0 on 8 GB system RAM
- IBM Granite 4.1 on 8 GB system RAM
- IBM Granite 4.2 on 8 GB system RAM
- InternLM 3 on 8 GB system RAM
- LFM2.5 on 8 GB system RAM
GLM-5 / GLM-5.1 on GPUs
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5090
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5070
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060 Ti 8GB
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for coding
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant
Model & Tools
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