Today · released October 16
Hugging Face says Beam needs four B200s; it isn't measured
Reflection AI releases Beam, an open-weight model with 501 billion parameters, of which each token uses 23 billion. Hugging Face's write-up says deployment must hold all 501 billion in memory and recommends starting at four B200s. The write-up says: "All 256 experts stay in memory, so deployment needs the memory of a 501B model but runs at the speed of a 23B one." (Hugging Face, 2026-10-16) NVFP4 is the 4-bit floating-point format used on NVIDIA Blackwell. Beam's NVFP4 checkpoint is about 280 GB, against 180 GB on a single B200. Hugging Face writes that two cards can hold the weights but leave almost no room for the cache needed during inference, hence the recommendation of four. The GPU cloud provider CoreWeave demonstrated fine-tuning directly on the NVFP4 checkpoint with LoRA, a lightweight fine-tuning method, and then serving it in the same format on Blackwell (CoreWeave, 2026-10-16). The October 12 issue covered Beam's training compute; what is new is the deployment threshold after release. Four B200s is Hugging Face's planning recommendation, not measured throughput. CoreWeave's demonstration is the vendor's own account, with no independent reproduction. How we checked →

Source and checks
This item ran in the brief of Oct 17, 2026, alongside 15 other items. How we checked it, the quote checks and what was still missing that day are on that issue's How we checked page.
Cite this item
SecondSource (2026). "Hugging Face says Beam needs four B200s; it isn't measured." https://secondsource.io/en/news/2026-10-17-6/. Accessed access date.