Kimi K3 contains 2.8 tln parameters, routing each token to 16 of 896 experts and using MXFP4 weights with MXFP8 activations. The sparse-expert design cuts actual compute and low-precision weights reduce storage, but model scale still creates heavy VRAM demand. A single DGX B200 offers 1.44 TB of GPU memory—roughly comparable to the baseline volume of K3’s 4‑bit weights—but once caches, activations and runtime overhead are included it is unlikely to host the full model. B300 and MI355X single‑GPU configurations provide 288 GB VRAM and are better suited for large-model deployment. The bigger bottleneck is interconnect: multi‑B200 clusters can jointly hold the model but frequent cross‑GPU expert exchanges force heavy cross‑server traffic, which is materially less efficient than GB300 NVL72’s internal NVLink. Kimi therefore recommends supernodes of 64+ accelerators; high‑density deployments also require high‑speed interconnect, parallel software stacks, and upgraded power and cooling. In short: reduced compute, high memory footprint, and heavy interconnect requirements.(金十数据)