Can I Run Llama 3.2 Family on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Yes

Yes — Llama 3.2 3B Instruct at Q8_0 needs about 5.2 GB of the 6.4 GB usable on 8 GB system RAM (~1.2 GB spare), at ~16.7 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~16.7 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

Llama 3.2 Family on 8 GB system RAM: memory by quantization

Quant	Memory needed	Fits 6.4 GB?	Max context	Est. speed	Download
F16	8.2 GB	✗ No	—	—	6.4 GB
Q8_0	5.2 GB	✓ Yes	16K	~16.7 tok/s	3.4 GB
Q6_K	4.4 GB	✓ Yes	16K	~20.7 tok/s	2.6 GB
Q5_K_M	4 GB	✓ Yes	16K	~23.2 tok/s	2.3 GB
Q4_K_M	3.7 GB	✓ Yes	16K	~26.3 tok/s	1.9 GB
Q3_K_M	3.1 GB	✓ Yes	32K	~33.8 tok/s	1.4 GB
Q2_K	2.8 GB	✓ Yes	32K	~40.1 tok/s	1.1 GB

Which Llama 3.2 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 90B Vision Instruct	57.8 GB	✗ Too large	—
Llama 3.2 11B Vision Instruct	8.5 GB	✗ Too large	—
Llama 3.2 3B Instruct	3.7 GB	✓ Fits	~26.3 tok/s
Llama 3.2 1B Instruct	1.8 GB	✓ Fits	~65 tok/s

What to watch out for

Only ~1.2 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
2 larger variants of Llama 3.2 Family do not fit and would need CPU offload or different hardware.
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 computed from this model's published attention configuration.

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