Can I Run Llama 3.3 on 192 GB system RAM?

Superseded model. Llama 3.3 has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

作者： Jakub Rusinowski · 最后更新： 2024年12月8日

Technically yes, but not recommended

It loads, but it is not worth running — Llama 3.3 70B Instruct at Q8_0 fits in 192 GB system RAM's 153.6 GB, yet the memory bandwidth limits it to ~0.9 tok/s (estimated), well below usable interactive speed.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~0.9 tok/s

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192 GB system RAM — what it gives a model

Usable memory for models	153.6 GB
Memory bandwidth	90 GB/s

Llama 3.3 on 192 GB system RAM: memory by quantization

Quant	Memory needed	Fits 153.6 GB?	Max context	Est. speed	Download
F16	143.5 GB	✓ Yes	32K	~0.5 tok/s	140 GB
Q8_0	77.9 GB	✓ Yes	128K	~0.9 tok/s	74.4 GB
Q6_K	60.9 GB	✓ Yes	128K	~1.1 tok/s	57.4 GB
Q5_K_M	53.1 GB	✓ Yes	128K	~1.3 tok/s	49.6 GB
Q4_K_M	45.7 GB	✓ Yes	128K	~1.5 tok/s	42.3 GB
Q3_K_M	33.3 GB	✓ Yes	128K	~2.1 tok/s	29.8 GB
Q2_K	26.5 GB	✓ Yes	128K	~2.7 tok/s	23 GB

What to watch out for

At ~0.9 tok/s this loads but is too slow for interactive use — expect roughly 67 seconds per 60 tokens.
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.
153.6 GB of the 192 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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