Sizing: which model on which machine

This section puts 7 open models against the 8 platforms in the catalogue.

Short answer. 41 model and machine combinations produce a configuration that fits. The matrix below gives, for each, the most precise format that loads the model.

Which model fits on which machine?

Each cell gives the most precise format that loads the model on that machine, and links to the detail page with the footprint in all four formats and the remaining margin.

Model	DGX Spark	Mac Studio Ultra	DGX Station	RTX PRO 6000 server	H200 SXM server	B200 SXM	B300 SXM	GB300 NVL72
GLM 5.2	does not fit	NVFP4	NVFP4	NVFP4	FP8	FP8	FP16	FP16
Kimi K3	does not fit	does not fit	does not fit	does not fit	does not fit	does not fit	NVFP4	FP16
Kimi K2.7 Code	does not fit	does not fit	NVFP4	NVFP4	NVFP4	FP8	FP16	FP16
DeepSeek V4	does not fit	does not fit	does not fit	does not fit	NVFP4	NVFP4	FP8	FP16
Nemotron 3 Ultra	does not fit	NVFP4	FP8	FP8	FP8	FP16	FP16	FP16
MiniMax M3	does not fit	FP8	FP8	FP8	FP16	FP16	FP16	FP16
Qwen 3.6 27B	FP16	FP16	FP16	FP16	FP16	FP16	FP16	FP16

How to read these figures

The footprint covers the model weights. In a mixture-of-experts architecture every expert stays resident in memory even though only a fraction computes on each token: active parameters govern speed, not occupancy. The attention cache sits on top and grows with context and with the number of concurrent requests.

Method

Footprints are calculated from the parameter count and the format, with a runtime margin. They are not measured on hardware. The attention cache sits on top and depends on context and concurrency. Prices are indicative and not contractual.