How fast will Qwen 3.5/3.6 35B A3B feel on your hardware?

Qwen 3.5/3.6 35B A3B speed on GPUs and Macs

Qwen 3.5/3.6 35B A3B is a 35.95B-parameter mixture-of-experts model (3B active per token). Smaller quantizations need less memory and run faster, at some cost in quality. Below is the expected speed on each piece of hardware at the best quantization that fits.

HardwareQuantizationMemoryGenerationFirst token
H200 141GB (cloud)Q4_K_MFits1048 tok/s0.3 s
H100 80GB (cloud)Q4_K_MFits731 tok/s0.3 s
A100 80GB (cloud)Q4_K_MFits445 tok/s0.3 s
NVIDIA RTX 5090 32GBQ4_K_MFits391 tok/s0.3 s
NVIDIA RTX PRO 6000 96GBQ4_K_MFits391 tok/s0.3 s
Mac Studio M5 Ultra 512GBQ4_K_MFits307 tok/s0.3 s
AMD RX 7900 XTX 24GBQ4_K_MPartial offload302 tok/s1.2 s
Mac Studio M3 Ultra 512GBQ4_K_MFits230 tok/s0.3 s
NVIDIA RTX 4090 24GBQ4_K_MPartial offload191 tok/s1.2 s
NVIDIA RTX 3090 24GBQ4_K_MPartial offload179 tok/s1.2 s
Mac Studio M4 Max 128GBQ4_K_MFits154 tok/s0.3 s
AMD Radeon AI PRO R9700 32GBQ4_K_MFits147 tok/s0.3 s
AMD RX 7900 XT 20GBQ4_K_MPartial offload122 tok/s1.2 s
MacBook M3 Max 48GBQ4_K_MFits106 tok/s0.3 s
MacBook Pro M5 Max 128GBQ4_K_MFits95 tok/s0.3 s
MacBook Pro M5 Pro 48GBQ4_K_MFits86 tok/s0.3 s
Mac Mini M4 Pro 64GBQ4_K_MFits77 tok/s0.3 s
AMD RX 9070 XT 16GBQ4_K_MPartial offload77 tok/s1.2 s
NVIDIA RTX 5080 16GBQ4_K_MPartial offload73 tok/s1.2 s
NVIDIA RTX 5070 Ti 16GBQ4_K_MPartial offload72 tok/s1.2 s
NVIDIA RTX 4070 Ti Super 16GBQ4_K_MPartial offload67 tok/s1.2 s
Intel Arc Pro B70 32GBQ4_K_MFits66 tok/s0.3 s
NVIDIA DGX Spark 128GBQ4_K_MFits60 tok/s0.3 s
NVIDIA RTX 5060 Ti 16GBQ4_K_MPartial offload58 tok/s1.2 s
AMD Ryzen AI Max+ 395 128GB (Strix Halo)Q4_K_MFits53 tok/s0.3 s
NVIDIA RTX 4070 12GBQ4_K_MPartial offload50 tok/s1.2 s
NVIDIA RTX 3060 12GBQ4_K_MPartial offload46 tok/s1.2 s
CPU-only (Ryzen 9 9950X, 96GB DDR5)Q4_K_MFits33 tok/s0.4 s
MacBook M4 Pro 24GBQ4_K_MDoesn't fitWon't run—
MacBook M2 Pro 16GBQ4_K_MDoesn't fitWon't run—

Other models

Estimates for a short prompt on a single device. Measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and more); others are derived from similar setups or projected from specs. Open source on GitHub.