Can I Run VibeThinker on RTX 3090 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 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~19.1 GB spare and running at ~133 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~133 tok/s

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	24 GB
Memory bandwidth	936 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

VibeThinker on RTX 3090 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	~85.9 tok/s	6.2 GB
Q8_0	4.9 GB	✓ Yes	128K	~133 tok/s	3.3 GB
Q6_K	4.2 GB	✓ Yes	128K	~155 tok/s	2.5 GB
Q5_K_M	3.8 GB	✓ Yes	128K	~167.7 tok/s	2.2 GB
Q4_K_M	3.5 GB	✓ Yes	128K	~181.8 tok/s	1.9 GB
Q3_K_M	3 GB	✓ Yes	128K	~211.9 tok/s	1.3 GB
Q2_K	2.7 GB	✓ Yes	128K	~233.1 tok/s	1 GB

Which VibeThinker sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
VibeThinker 3B	3.5 GB	✓ Fits	~181.8 tok/s
VibeThinker 1.5B	2.4 GB	✓ Fits	~247 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 3090 desktop limitations

The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
Older architecture: no FP8 acceleration, and higher idle power than a current 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 3090 at 936 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.

FAQ