Can the RTX 5090 run Qwen3.6 27B?

Yes. Qwen3.6 27B fits the RTX 5090's 32 GB at Q4_K_M (about 17.8 GB needed), running at an estimated 64 tok/s. There's room for up to Q5_K_M if you want higher quality.

VRAM
32 GB
Needs at Q4
17.8 GB
Headroom
14.2 GB
Speed (Q4_K_M)
~64 tok/s

Every quantization

Qwen3.6 27B (28B) on 32 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M16 GB17.8 GBFits easily~64 tok/s
Q5_K_M20 GB21.8 GBFits~51 tok/s
Q8_031 GB32.8 GBToo big -
FP1656 GB57.8 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 5090's 1792 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

ollama run qwen3.6:27b
llama-server -hf unsloth/Qwen3.6-27B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.6 27B details →

Compare

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

Qwen3.6 27B on other hardware

Also runs on the RTX 5090

Frequently asked

Quick answers

Can the NVIDIA RTX 5090 run Qwen3.6 27B?

Yes. Qwen3.6 27B fits the RTX 5090's 32 GB at Q4_K_M (about 17.8 GB needed), running at an estimated 64 tok/s. There's room for up to Q5_K_M if you want higher quality.

How much VRAM does Qwen3.6 27B need?

About 16 GB of weights at Q4_K_M, 31 GB at Q8_0 and 56 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3.6 27B on the RTX 5090?

Roughly 64 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5090's 1792 GB/s memory bandwidth.