Can I Run Qwen3.8 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes, but it is tight

Yes, but it is tight — Qwen3.8 27B at Q5_K_M needs about 22.2 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~1.8 GB before the runtime starts swapping. Expect ~34 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: low · Recommended quantization: Q5_K_M · Estimated speed: ~34 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

Qwen3.8 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	58.1 GB	✗ No	—	—	55.6 GB
Q8_0	32.1 GB	✗ No	—	—	29.5 GB
Q6_K	25.3 GB	✗ No	—	—	22.8 GB
Q5_K_M	22.2 GB	✓ Yes	16K	~34 tok/s	19.7 GB
Q4_K_M	19.3 GB	✓ Yes	16K	~39.1 tok/s	16.8 GB
Q3_K_M	14.4 GB	✓ Yes	32K	~52.5 tok/s	11.8 GB
Q2_K	11.7 GB	✓ Yes	64K	~64.6 tok/s	9.1 GB

Which Qwen3.8 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3.8-Max	1457.5 GB	✗ Too large	—
Qwen3.8 27B	19.3 GB	✓ Fits	~39.1 tok/s

What to watch out for

1 larger variant of Qwen3.8 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.

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 inferred from the parameter count because this model does not publish its full attention configuration.

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