How fast will Nemotron 3.5 Lightning 30B A3B feel on your hardware?

Nemotron 3.5 Lightning 30B A3B speed on GPUs and Macs

Nemotron 3.5 Lightning 30B A3B is a 31.58B-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_MFits871 tok/s0.3 s
H100 80GB (cloud)Q4_K_MFits608 tok/s0.3 s
NVIDIA RTX 5090 32GBQ4_K_MFits385 tok/s0.3 s
NVIDIA RTX PRO 6000 96GBQ4_K_MFits325 tok/s0.3 s
AMD Instinct MI50 32GBQ4_K_MFits274 tok/s0.4 s
Mac Studio M5 Ultra 256GBQ4_K_MFits267 tok/s0.3 s
Mac Studio M5 Ultra 512GBQ4_K_MFits240 tok/s0.3 s
Mac Studio M3 Ultra 512GBQ4_K_MFits182 tok/s0.3 s
AMD Radeon AI PRO R9700 32GBQ4_K_MFits173 tok/s0.3 s
NVIDIA Tesla V100 32GBQ4_K_MFits163 tok/s0.3 s
MacBook Pro M5 Max 128GBQ4_K_MFits137 tok/s0.3 s
Mac Studio M4 Max 128GBQ4_K_MFits121 tok/s0.3 s
AMD RX 7900 XTX 24GBQ4_K_MPartial offload96 tok/s1.2 s
NVIDIA RTX 4090 24GBQ4_K_MPartial offload85 tok/s1.2 s
NVIDIA RTX 3090 24GBQ4_K_MPartial offload83 tok/s1.2 s
AMD Ryzen AI Max+ PRO 495 192GB (Gorgon Halo)Q4_K_MFits73 tok/s0.3 s
Mac mini M5 Pro 64GBQ4_K_MFits68 tok/s0.3 s
MacBook Pro M5 Pro 48GBQ4_K_MFits68 tok/s0.3 s
AMD Ryzen AI Max+ 395 128GB (Strix Halo)Q4_K_MFits64 tok/s0.3 s
AMD RX 7900 XT 20GBQ4_K_MPartial offload57 tok/s1.2 s
NVIDIA RTX Spark N1X 128GB (laptop)Q4_K_MFits54 tok/s0.3 s
Intel Arc Pro B70 32GBQ4_K_MFits53 tok/s0.3 s
NVIDIA DGX Spark 128GBQ4_K_MFits50 tok/s0.3 s
AMD RX 9070 XT 16GBQ4_K_MPartial offload41 tok/s1.2 s
NVIDIA RTX 5080 16GBQ4_K_MPartial offload41 tok/s1.2 s
NVIDIA RTX 5070 Ti 16GBQ4_K_MPartial offload40 tok/s1.2 s
NVIDIA RTX 5060 Ti 16GBQ4_K_MPartial offload35 tok/s1.2 s
NVIDIA RTX 3060 12GBQ4_K_MPartial offload29 tok/s1.2 s
CPU-only (Ryzen 9 9950X, 96GB DDR5)Q4_K_MFits25 tok/s0.4 s
MacBook M4 Pro 24GBQ4_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.