Unverified50% confidenceFactExact time
Beam's VRAM requirement is close to that of a 501B-scale model, while its compute requirement is close to that of a 23B-scale model.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBeam's total parameter count is 501B, with 23B parameters activated per token (about 4.6% of the network is activated).77% similarUnverifiedBeam adopts a Sparse Mixture-of-Experts (Sparse MoE) architecture with 52 layers, activating only 23 billion parameters at a time.72% similarUnverified万象模型提供了 1.3B 和 14B 参数量的不同规模版本68% similarUnverifiedMixtral 8×22B has 141B total parameters, but only approximately 39B are activated per inference.65% similarUnverified27B stands for 27 billion parameters, and the parameter count determines the upper limit of a model's capacity.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1048022API
curl https://kongchang.com/api/v1/knowledge/claims/1048022MCP
get_claim(id=1048022)