Can RTX 4090 24GB run Qwen3.6 35B-A3B?
Measured results for Qwen3.6 35B-A3B (35.95B MoE, 3B active) on RTX 4090 24GB, plus what fits and the commands to run it.
Short answer
Yes. Qwen3.6 35B-A3B UD-Q4_K_M fits fully on RTX 4090 24GB at 16k context, using 22.5 of 23.0 GB usable, and generates about 190.5 tokens/s in our own run. For UD-Q4_K_M, our bandwidth-only estimate was ~300 tok/s against a measured median of ~190 (58% too optimistic).
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 190.5 | 190.5–190.5 | 7962 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 190.5 | 3 repeats | 7962 | 3 | own measurement |
Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench b11658 on a rented GPU.
Speed vs context depth measured
Our own llama-bench runs on GeForce RTX 4090 (flash attention on, 3 repeats): generation and prompt speed with the given number of tokens already in the context.
| Quant | Context depth | Generation tok/s | vs depth 0 | Prompt tok/s |
|---|---|---|---|---|
| Q4_K_M | 0 | 190.5 | 100% | 7962 |
| Q4_K_M | 4k | 187.1 | 98% | 7967 |
| Q4_K_M | 16k | 177.3 | 93% | 7198 |
Memory and context limits
UD-Q4_K_M weights are 22.1 GB; the 16k f16 KV cache adds 0.40 GB. Largest context that fits in 23.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 32k |
| q8_0 | 64k |
| q4_0 | 128k |
Native limit 256k, 1.01M with YaRN. Full KV cache table for Qwen3.6 35B-A3B.
Copy-paste commands
The advisor's pick for this hardware: UD-Q4_K_M, 16k context.
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
FAQ
How fast is Qwen3.6 35B-A3B on RTX 4090 24GB?
About 190.5 tokens/s for generation (Q4_K_M on llama.cpp CUDA, 128-token generation), 7962 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Qwen3.6 35B-A3B need on RTX 4090 24GB?
The advisor's pick, UD-Q4_K_M, is 22.1 GB of weights plus 0.40 GB of KV cache at 16k context (22.5 GB), against 23.0 GB usable.
What is the maximum context for Qwen3.6 35B-A3B on RTX 4090 24GB?
With an f16 KV cache about 32k tokens, 64k with q8_0 and 128k with q4_0, bounded by the model's own limit.
Related
All models on RTX 4090 24GB · Qwen3.6 35B-A3B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 3090 24GB vs RTX 4090 24GB · RTX 4090 24GB vs RTX 5090 32GB · RTX 4090 24GB vs AMD RX 7900 XTX 24GB · RTX 5080 16GB vs RTX 4090 24GB