Can the RTX 4090 run Qwen3.6 35B A3B?
Just. Qwen3.6 35B A3B at Q4_K_M needs about 22.8 GB of the RTX 4090's 24 GB, running at an estimated 238 tok/s. Keep context short and close other GPU apps.
Every quantization
Qwen3.6 35B A3B (36B, 3B active) on 24 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 21 GB | 22.8 GB | Tight | ~238 tok/s |
| Q5_K_M | 26 GB | 27.8 GB | Too big | - |
| Q8_0 | 39 GB | 40.8 GB | Too big | - |
| FP16 | 72 GB | 73.8 GB | Too 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
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF -ngl 99Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.6 35B A3B details →
Compare
Same model, other hardware - and other models on this card
Qwen3.6 35B A3B on other hardware
- RTX 5090~391 tok/s
- RTX 5090 D~391 tok/s
- RTX 6000D~343 tok/s
- Radeon RX 7900 XTX~210 tok/s
- RTX 3090~209 tok/s
- MacBook Pro 16" M4 Max~119 tok/s
- Arc Pro B60~100 tok/s
- DGX Spark (GB10)~60 tok/s
Also runs on the RTX 4090
- GLM-4.7-Flash~241 tok/s
- Qwen3-30B-A3B-Instruct-2507~236 tok/s
- Qwen3.6 27B~39 tok/s
- Qwen3.8 27B~39 tok/s
- Gemma 4 26B A4B~184 tok/s
- gpt-oss-20b~208 tok/s
- Qwen2.5-Coder-14B-Instruct~70 tok/s
- Qwen3 14B~78 tok/s
Frequently asked
Quick answers
Can the NVIDIA RTX 4090 run Qwen3.6 35B A3B?
Just. Qwen3.6 35B A3B at Q4_K_M needs about 22.8 GB of the RTX 4090's 24 GB, running at an estimated 238 tok/s. Keep context short and close other GPU apps.
How much VRAM does Qwen3.6 35B A3B need?
About 21 GB of weights at Q4_K_M, 39 GB at Q8_0 and 72 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is Qwen3.6 35B A3B on the RTX 4090?
Roughly 238 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; as a mixture-of-experts model only about 3B parameters are read per token, which is why it is faster than its size suggests.