Can I Run Command R Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2024年4月4日

Yes, but it is tight

Yes, but it is tight — Command R (35B) at Q2_K needs about 23 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~1 GB before the runtime starts swapping. Expect ~40.7 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~40.7 tok/s

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RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	24 GB
Memory bandwidth	1008 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Command R Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization

Quant	Memory needed	Fits 24 GB?	Max context	Est. speed	Download
F16	81.5 GB	✗ No	—	—	70 GB
Q8_0	48.7 GB	✗ No	—	—	37.2 GB
Q6_K	40.2 GB	✗ No	—	—	28.7 GB
Q5_K_M	36.3 GB	✗ No	—	—	24.8 GB
Q4_K_M	32.7 GB	✗ No	—	—	21.1 GB
Q3_K_M	26.5 GB	✗ No	—	—	14.9 GB
Q2_K	23 GB	✓ Yes	8K	~40.7 tok/s	11.5 GB

Newer alternative. Cohere has not released a newer model in this library. If you are choosing today, Mistral Small 4 is a comparable current option. Mistral Small 4 needs at least 75.5 GB at Q4_K_M with an 8K context, so it does not fit the RTX 4090 Desktop (24 GB VRAM, 64 GB RAM).

Which Command R Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Command R+ (104B)	65.7 GB	✗ Too large	—
Command R (35B)	32.7 GB	✗ Too large	—

What to watch out for

Only ~1 GB of headroom at Q2_K: a longer context or a second application can push this into swapping.
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.
2 larger variants of Command R Family do not fit and would need CPU offload or different hardware.

RTX 4090 desktop limitations

24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second card.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
24 GB of VRAM on the NVIDIA GeForce RTX 4090 at 1008 GB/s.
64 GB of system RAM available for CPU offload when a model exceeds VRAM.
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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