Can I Run Yi 1.5 Family on 96 GB system RAM?

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作者： Jakub Rusinowski · 最后更新： 2024年5月13日

Yes — comfortably

Yes, comfortably — Yi 1.5 34B Chat at Q2_K needs about 14.1 GB of the 76.8 GB usable on 96 GB system RAM, leaving ~62.7 GB spare and running at ~5.4 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~5.4 tok/s

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96 GB system RAM — what it gives a model

Usable memory for models	76.8 GB
Memory bandwidth	90 GB/s

Yi 1.5 Family on 96 GB system RAM: memory by quantization

Quant	Memory needed	Fits 76.8 GB?	Max context	Est. speed	Download
F16	71.6 GB	✓ Yes	16K	~1 tok/s	68.8 GB
Q8_0	39.4 GB	✓ Yes	16K	~1.8 tok/s	36.6 GB
Q6_K	31 GB	✓ Yes	16K	~2.3 tok/s	28.2 GB
Q5_K_M	27.2 GB	✓ Yes	16K	~2.6 tok/s	24.4 GB
Q4_K_M	23.6 GB	✓ Yes	16K	~3.1 tok/s	20.8 GB
Q3_K_M	17.5 GB	✓ Yes	16K	~4.2 tok/s	14.7 GB
Q2_K	14.1 GB	✓ Yes	16K	~5.4 tok/s	11.3 GB

Which Yi 1.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Yi 1.5 34B Chat	23.6 GB	✓ Fits	~3.1 tok/s
Yi 1.5 9B Chat	6.9 GB	✓ Fits	~11.4 tok/s

What to watch out for

Q2_K 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.
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.
76.8 GB of the 96 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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