Best home rig for Qwen3.6 35B-A3B
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Real, measured generation speeds for Qwen3.6 35B-A3B on home hardware, fastest first. Different quants/engines aren't directly comparable.
| Build | Quant | Engine | tok/s | Cost | |
|---|---|---|---|---|---|
| RTX 5090: 2026 models (llamabench community) | llama-bench tg128 (quant per result) | llama.cpp | 258 | source | |
| 4x RTX 3090 lab: Qwen3.6 on vLLM vs llama.cpp | AWQ-INT4 + MTP-3 (TP=2) | vLLM | 225-231 | source | |
| 4x RTX 3090 lab: Qwen3.6 on vLLM vs llama.cpp | AWQ-INT4 (TP=2) | vLLM | 149 | source | |
| RTX 3090: 2026 models (llamabench community) | llama-bench tg128 (quant per result) | llama.cpp | 146-153 | source | |
| Single RTX 3090: Qwen3.6 35B-A3B (Giles Thomas) | Q4 GGUF, all layers on GPU, ~90K ctx | llama.cpp CUDA | 139.6 | source | |
| 4x RTX 3090 lab: Qwen3.6 on vLLM vs llama.cpp | IQ4_XS (1 GPU) | llama.cpp | 115-133 | source | |
| HP OmniBook Ultra 16 (RTX Spark N1X, 128GB) laptop: local AI benchmarks | Ollama default, 64K context, Codex CLI coding agent | Ollama | 89.5 | $6,230 (≈€5,559) | source |
| Strix Halo (Radeon 8060S, 96 GB): 2026 models (llamabench community) | llama-bench tg128 (quant per result) | llama.cpp | 50.2-62.4 | source |
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Rent before you buy: try this model on a cloud GPU for a few dollars first: RunPod
FAQ
How much VRAM does Qwen3.6 35B-A3B need?
The weights are about 21.7 GB at Q4_K_M (35.51B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is RTX 3090 (24 GB) at 146-153 tok/s.
What is the fastest measured hardware for Qwen3.6 35B-A3B?
RTX 5090: 258 tok/s generation (llama.cpp, llama-bench tg128 (quant per result)), from https://llamabench.ai/browse/rtx5090.
Which GPUs have measured results for Qwen3.6 35B-A3B?
2x RTX 3090, RTX 3090, RTX 5090, RTX Spark N1X.
Can it run on… 2x RTX 3090 · RTX 3090 · RTX 5090 · RTX Spark N1X
Related: what GPU do you need for 2026 models · all builds