Can I Run Cosmos 3 on 48 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年7月21日

Yes — comfortably

Yes, comfortably — Cosmos 3 Nano at Q8_0 needs about 19.2 GB of the 38.4 GB usable on 48 GB system RAM, leaving ~19.2 GB spare and running at ~7.2 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~7.2 tok/s

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48 GB system RAM — what it gives a model

Usable memory for models	38.4 GB
Memory bandwidth	90 GB/s

Cosmos 3 on 48 GB system RAM: memory by quantization

Quant	Memory needed	Fits 38.4 GB?	Max context	Est. speed	Download
F16	34.2 GB	✓ Yes	16K	~4 tok/s	32 GB
Q8_0	19.2 GB	✓ Yes	16K	~7.2 tok/s	17 GB
Q6_K	15.4 GB	✓ Yes	16K	~9 tok/s	13.1 GB
Q5_K_M	13.6 GB	✓ Yes	16K	~10.3 tok/s	11.3 GB
Q4_K_M	11.9 GB	✓ Yes	16K	~11.8 tok/s	9.7 GB
Q3_K_M	9.1 GB	✓ Yes	16K	~15.7 tok/s	6.8 GB
Q2_K	7.5 GB	✓ Yes	16K	~19.2 tok/s	5.3 GB

Which Cosmos 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Cosmos 3 Super	41.8 GB	✗ Too large	—
Cosmos 3 Nano	11.9 GB	✓ Fits	~11.8 tok/s
Cosmos 3 Edge	4.1 GB	✓ Fits	~37.1 tok/s

What to watch out for

1 larger variant of Cosmos 3 does 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.
38.4 GB of the 48 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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