Can I Run Phi-4 Mini on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2025年2月4日

Yes, but it is tight

Yes, but it is tight — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 6.4 GB usable on 8 GB system RAM, leaving only ~0.5 GB before the runtime starts swapping. Expect ~14.2 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~14.2 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

Phi-4 Mini on 8 GB system RAM: memory by quantization

Quant	Memory needed	Fits 6.4 GB?	Max context	Est. speed	Download
F16	9.5 GB	✗ No	—	—	7.6 GB
Q8_0	5.9 GB	✓ Yes	8K	~14.2 tok/s	4 GB
Q6_K	5 GB	✓ Yes	16K	~17.7 tok/s	3.1 GB
Q5_K_M	4.6 GB	✓ Yes	16K	~19.9 tok/s	2.7 GB
Q4_K_M	4.2 GB	✓ Yes	16K	~22.5 tok/s	2.3 GB
Q3_K_M	3.5 GB	✓ Yes	16K	~29.1 tok/s	1.6 GB
Q2_K	3.1 GB	✓ Yes	32K	~34.7 tok/s	1.2 GB

What to watch out for

Only ~0.5 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
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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