Can the RTX 4090 run Qwen3 32B?

Just. Qwen3 32B at Q4_K_M needs about 20.8 GB of the RTX 4090's 24 GB, running at an estimated 33 tok/s. Keep context short and close other GPU apps.

VRAM
24 GB
Needs at Q4
20.8 GB
Headroom
3.2 GB
Speed (Q4_K_M)
~33 tok/s

Every quantization

Qwen3 32B (32B) on 24 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M19 GB20.8 GBTight~33 tok/s
Q5_K_M23 GB24.8 GBToo big -
Q8_034 GB35.8 GBToo big -
FP1664 GB65.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 4090's 1008 GB/s bandwidth (XiongjieDai/GPU-Benchmarks-on-LLM-Inference). Long contexts need more memory and run slower.

Run it

Copy, paste, go

ollama run qwen3:32b
llama-server -hf Qwen/Qwen3-32B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3 32B details →

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Same model, other hardware - and other models on this card

Qwen3 32B on other hardware

Also runs on the RTX 4090

Frequently asked

Quick answers

Can the NVIDIA RTX 4090 run Qwen3 32B?

Just. Qwen3 32B at Q4_K_M needs about 20.8 GB of the RTX 4090's 24 GB, running at an estimated 33 tok/s. Keep context short and close other GPU apps.

How much VRAM does Qwen3 32B need?

About 19 GB of weights at Q4_K_M, 34 GB at Q8_0 and 64 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3 32B on the RTX 4090?

Roughly 33 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 4090's 1008 GB/s memory bandwidth.