HBF

Researchers at UC Berkeley, working with chipmaker FuriosaAI, have published work on HBF, a memory approach aimed at high-throughput serving of large language models. The piece, carried by Semiconductor Engineering, focuses on how new memory architectures could ease the bandwidth and cost bottlenecks that limit LLM inference at scale. The work is being followed by readers tracking hardware innovation for AI infrastructure.