Can I run Qwen3.6 35B-A3B on an RTX 4090?
Yes: Qwen3.6 35B-A3B needs about 23.5 GB at Q4_K_M with 32K tokens of context, which fits the 24 GB RTX 4090 with 542 MB to spare. Nothing more precise fits at 32K with 0.5 GB to spare; Q4_K_M runs up to 33K tokens on the RTX 4090.
Yes Q4_K_M with 32K tokens of context
- Q4_K_M, 32K
- 23.5 GB
- RTX 4090
- 24 GB, 1,008 GB/s
- To spare
- 542 MB
- Tokens/s
- 103–184 tokens/s
At Q4_K_M with 32K tokens of context it writes about 103–184 tokens/s for one request on an RTX 4090.
Best precision for Qwen3.6 35B-A3B on an RTX 4090
The most precise setting that leaves at least 0.5 GB free; one that fits with less is marked tight.
| Context | Best fit | Memory | Free | Tokens/s |
|---|---|---|---|---|
| 8K | Q4_K_M | 23.0 GB | 1.05 GB | 124–226 |
| 32K | Q4_K_M | 23.5 GB | 542 MB | 103–184 |
| 128K | Q3_K_M | 21.3 GB | 2.75 GB | 66–114 |
| 256K (full) | Q2_K | 21.4 GB | 2.58 GB | 43–73 |
Qwen3.6 35B-A3B on the RTX 4090 as the context fills
One request, FP16 KV cache, 0.5 GB plus 10% overhead; a minus sign is memory missing, and tight is less than 0.5 GB free.
| Context | Q4_K_M | Free | Tokens/s | Q8_0 | Free | Tokens/s |
|---|---|---|---|---|---|---|
| 4K | 22.9 GB | 1.13 GB | 129–234 | 39.7 GB | −15.7 GB | — |
| 8K | 23.0 GB | 1.05 GB | 124–226 | 39.8 GB | −15.8 GB | — |
| 16K | 23.1 GB | 894 MB | 116–210 | 40.0 GB | −16.0 GB | — |
| 32K | 23.5 GB | 542 MB | 103–184 | 40.3 GB | −16.3 GB | — |
| 64K | 24.2 GB | −162 MB | — | 41.0 GB | −17.0 GB | — |
| 128K | 25.5 GB | −1.53 GB | — | 42.4 GB | −18.4 GB | — |
| 256K (full) | 28.3 GB | −4.28 GB | — | 45.1 GB | −21.1 GB | — |
--n-cpu-moe for Qwen3.6 35B-A3B on the RTX 4090
The measured UD-Q4_K_M GGUF fits the RTX 4090 whole at 32K (21.7 GB), so --n-cpu-moe is not needed there. Measured UD-Q4_K_M file, 1 GB of buffers; tokens/s by system RAM speed.
Qwen3.6 35B-A3B on more than one RTX 4090
| Cards | Best at 32K | Tokens/s | Q4_K_M longest | Q4_K_M tokens/s | Rent per hour |
|---|---|---|---|---|---|
| 2× (48 GB) | Q8_0 | 101–195 | 256K (full) | 131–266 | $1.06 |
| 4× (96 GB) | BF16 | 111–217 | 256K (full) | 180–395 | $2.12 |
Tensor parallel with the combined bandwidth, as in vLLM; llama.cpp splits layers by default and runs at about one card's speed.
Other options
- The next smaller setting, Q3_K_M, takes 19.2 GB at 32K, 4.81 GB under the RTX 4090; it fits with 0.5 GB to spare up to 232K tokens.
- Renting an RTX 4090 costs about $0.53 an hour (median on getdeploying.com, 2026-09-29): $0.80–1.4 per million tokens at 103–184 tokens/s.
- Smallest setup for Q4_K_M at 32K: RTX 3090 (24 GB).
Run Qwen3.6 35B-A3B on the RTX 4090 with llama-server
llama-server -hf ggml-org/Qwen3.6-35B-A3B-GGUF:Q4_K_M -c 33792Qwen3.6-35B-A3B-Q4_K_M.gguf, 20.4 GB, from ggml-org/
Questions
Can I run Qwen3.6 35B-A3B on an RTX 4090?
Yes: Qwen3.6 35B-A3B needs about 23.5 GB at Q4_K_M with 32K tokens of context, which fits the 24 GB RTX 4090 with 542 MB to spare. Nothing more precise fits at 32K with 0.5 GB to spare; Q4_K_M runs up to 33K tokens on the RTX 4090.
How fast is Qwen3.6 35B-A3B on an RTX 4090?
At Q4_K_M with 32K tokens of context it writes about 103–184 tokens/s for one request on an RTX 4090.
Does Qwen3.6 35B-A3B need --n-cpu-moe on an RTX 4090?
The measured UD-Q4_K_M GGUF fits the RTX 4090 whole at 32K (21.7 GB), so --n-cpu-moe is not needed there.
What does a second RTX 4090 change for Qwen3.6 35B-A3B?
Two RTX 4090 cards (48 GB in one tensor-parallel group) hold Qwen3.6 35B-A3B at Q8_0 with 32K, and Q4_K_M up to 256K (full) tokens, at about 131–266 tokens/s.
Try other settings in the VRAM calculator, the speed calculator or the MoE offload planner. See also Qwen3.6 35B-A3B VRAM requirements, what LLMs an RTX 4090 can run and every pair, or detect your own GPU. Model data checked .