How fast will Muse Glimmer 30B feel on your hardware?

Muse Glimmer 30B speed on GPUs and Macs

Muse Glimmer 30B is a 29.78B-parameter model. 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_MFits75 tok/s0.3 s
H100 80GB (cloud)Q4_K_MFits53 tok/s0.3 s
A100 80GB (cloud)Q4_K_MFits32 tok/s0.3 s
Mac Studio M5 Ultra 512GBQ4_K_MFits29 tok/s0.4 s
NVIDIA RTX 5090 32GBQ4_K_MFits28 tok/s0.3 s
NVIDIA RTX PRO 6000 96GBQ4_K_MFits28 tok/s0.3 s
AMD RX 7900 XTX 24GBQ4_K_MFits28 tok/s0.5 s
Mac Studio M3 Ultra 512GBQ4_K_MFits20 tok/s0.5 s
AMD Radeon AI PRO R9700 32GBQ4_K_MFits19 tok/s0.4 s
AMD RX 7900 XT 20GBQ4_K_MPartial offload16 tok/s1.6 s
NVIDIA RTX 4090 24GBQ4_K_MFits16 tok/s0.3 s
NVIDIA RTX 3090 24GBQ4_K_MFits15 tok/s0.4 s
MacBook Pro M5 Max 128GBQ4_K_MFits15 tok/s0.4 s
Mac Studio M4 Max 128GBQ4_K_MFits13 tok/s0.6 s
MacBook M3 Max 48GBQ4_K_MFits9.5 tok/s0.6 s
AMD RX 9070 XT 16GBQ4_K_MPartial offload8.4 tok/s1.4 s
NVIDIA RTX 5080 16GBQ4_K_MPartial offload7.9 tok/s1.3 s
NVIDIA RTX 5070 Ti 16GBQ4_K_MPartial offload7.7 tok/s1.3 s
AMD Ryzen AI Max+ 395 128GB (Strix Halo)Q4_K_MFits7.4 tok/s0.7 s
MacBook Pro M5 Pro 48GBQ4_K_MFits7.3 tok/s0.6 s
NVIDIA RTX 4070 Ti Super 16GBQ4_K_MPartial offload7.0 tok/s1.3 s
Mac Mini M4 Pro 64GBQ4_K_MFits6.5 tok/s0.8 s
Intel Arc Pro B70 32GBQ4_K_MFits5.9 tok/s0.7 s
NVIDIA RTX 5060 Ti 16GBQ4_K_MPartial offload5.9 tok/s1.4 s
NVIDIA RTX 4070 12GBQ4_K_MPartial offload5.0 tok/s1.3 s
NVIDIA RTX 3060 12GBQ4_K_MPartial offload4.5 tok/s1.5 s
NVIDIA DGX Spark 128GBQ4_K_MFits4.3 tok/s0.3 s
CPU-only (Ryzen 9 9950X, 96GB DDR5)Q4_K_MFits3.0 tok/s1.0 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.