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

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes — comfortably

Yes, comfortably — VibeThinker 3B at Q8_0 needs about 4.9 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~19.1 GB spare and running at ~140 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~140 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

VibeThinker 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	7.8 GB	✓ Yes	128K	~91.1 tok/s	6.2 GB
Q8_0	4.9 GB	✓ Yes	128K	~140 tok/s	3.3 GB
Q6_K	4.2 GB	✓ Yes	128K	~162.5 tok/s	2.5 GB
Q5_K_M	3.8 GB	✓ Yes	128K	~175.5 tok/s	2.2 GB
Q4_K_M	3.5 GB	✓ Yes	128K	~189.8 tok/s	1.9 GB
Q3_K_M	3 GB	✓ Yes	128K	~220.1 tok/s	1.3 GB
Q2_K	2.7 GB	✓ Yes	128K	~241.3 tok/s	1 GB

Which VibeThinker sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
VibeThinker 3B	3.5 GB	✓ Fits	~189.8 tok/s
VibeThinker 1.5B	2.4 GB	✓ Fits	~255.1 tok/s

What to watch out for

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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