Can I Run Gemma 2 Family on 32 GB system RAM?

Superseded model. Gemma 2 Family has been superseded by Gemma 4. This page is kept for reference; the newer family is a better starting point. View Gemma 4 →

作者： Jakub Rusinowski · 最后更新： 2024年6月27日

Yes — comfortably

Yes, comfortably — Gemma 2 9B IT at Q8_0 needs about 13.2 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~12.4 GB spare and running at ~6 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~6 tok/s

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32 GB system RAM — what it gives a model

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

Gemma 2 Family on 32 GB system RAM: memory by quantization

Quant	Memory needed	Fits 25.6 GB?	Max context	Est. speed	Download
F16	21.6 GB	✓ Yes	8K	~3.4 tok/s	18 GB
Q8_0	13.2 GB	✓ Yes	8K	~6 tok/s	9.6 GB
Q6_K	11 GB	✓ Yes	8K	~7.5 tok/s	7.4 GB
Q5_K_M	10 GB	✓ Yes	8K	~8.5 tok/s	6.4 GB
Q4_K_M	9.1 GB	✓ Yes	8K	~9.6 tok/s	5.4 GB
Q3_K_M	7.5 GB	✓ Yes	8K	~12.5 tok/s	3.8 GB
Q2_K	6.6 GB	✓ Yes	8K	~14.9 tok/s	3 GB

What to watch out for

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 computed from this model's published attention configuration.

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