Can I Run IBM Granite 4.0 on 24 GB system RAM?
作者： Jakub Rusinowski · 最后更新： 2026年8月15日
Yes
Yes — Granite 4.0 Small-H 32B-A9B at Q3_K_M needs about 16.3 GB of the 19.2 GB usable on 24 GB system RAM (~2.9 GB spare), at ~13.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~13.7 tok/s
24 GB system RAM — what it gives a model
| Usable memory for models | 19.2 GB |
| Memory bandwidth | 90 GB/s |
IBM Granite 4.0 on 24 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 19.2 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 | ✗ No | — | — | 22.7 GB |
| Q4_K_M | 22 GB | ✗ No | — | — | 19.3 GB |
| Q3_K_M | 16.3 GB | ✓ Yes | 16K | ~13.7 tok/s | 13.6 GB |
| Q2_K | 13.2 GB | ✓ Yes | 32K | ~16.7 tok/s | 10.5 GB |
What to watch out for
- Q3_K_M 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.
- 1 larger variant of IBM Granite 4.0 does 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.
- 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.
FAQ
Can I run IBM Granite 4.0 on 24 GB system RAM?
Yes — Granite 4.0 Small-H 32B-A9B at Q3_K_M needs about 16.3 GB of the 19.2 GB usable on 24 GB system RAM (~2.9 GB spare), at ~13.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of IBM Granite 4.0 should I use on 24 GB system RAM?
Q3_K_M — it needs about 16.3 GB of the 19.2 GB available, downloads as roughly 13.6 GB, and runs at an estimated 13.7 tokens/sec with up to 16K of context.
What limits IBM Granite 4.0 on 24 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 24 GB system RAM
IBM Granite 4.0 on GPUs
What This Model Is Good At
Model & Tools
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