Can the RTX 4090 run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 4090's 24 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 209 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
24 GB
Needs at Q4
4.8 GB
Headroom
19.2 GB
Speed (Q4_K_M)
~209 tok/s

Every quantization

Qwen3.5 4B (4.7B) on 24 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M3 GB4.8 GBFits easily~209 tok/s
Q5_K_M3 GB4.8 GBFits easily~209 tok/s
Q8_05 GB6.8 GBFits easily~125 tok/s
FP169 GB10.8 GBFits easily~70 tok/s

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

llama-server -hf unsloth/Qwen3.5-4B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.5 4B details →

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

Qwen3.5 4B on other hardware

Also runs on the RTX 4090

Frequently asked

Quick answers

Can the NVIDIA RTX 4090 run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 4090's 24 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 209 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3.5 4B need?

About 3 GB of weights at Q4_K_M, 5 GB at Q8_0 and 9 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3.5 4B on the RTX 4090?

Roughly 209 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.