Can I Run Gemma 4 on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年6月3日

Yes, but it is tight

Yes, but it is tight — Gemma 4 E2B at Q6_K needs about 6 GB of the 6.4 GB usable on 8 GB system RAM, leaving only ~0.4 GB before the runtime starts swapping. Expect ~13.9 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q6_K · Estimated speed: ~13.9 tok/s

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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

Gemma 4 on 8 GB system RAM: memory by quantization

Quant	Memory needed	Fits 6.4 GB?	Max context	Est. speed	Download
F16	12 GB	✗ No	—	—	10.2 GB
Q8_0	7.2 GB	✗ No	—	—	5.4 GB
Q6_K	6 GB	✓ Yes	8K	~13.9 tok/s	4.2 GB
Q5_K_M	5.4 GB	✓ Yes	8K	~15.7 tok/s	3.6 GB
Q4_K_M	4.9 GB	✓ Yes	16K	~18 tok/s	3.1 GB
Q3_K_M	4 GB	✓ Yes	16K	~23.8 tok/s	2.2 GB
Q2_K	3.5 GB	✓ Yes	16K	~28.8 tok/s	1.7 GB

Which Gemma 4 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Gemma 4 31B	21.3 GB	✗ Too large	—
Gemma 4 26B-A4B	18.2 GB	✗ Too large	—
Gemma 4 12B (Unified)	9.4 GB	✗ Too large	—
Gemma 4 E4B	6.8 GB	✗ Too large	—
Gemma 4 E2B	4.9 GB	✓ Fits	~18 tok/s

What to watch out for

Only ~0.4 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
4 larger variants of Gemma 4 do 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.
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.

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