HBF for High-Throughput LLM Serving (UC Berkeley, FuriosaAI)

Researchers at the UC Berkeley and FuriosaAI published a technical paper titled “Characterizing High Bandwidth Flash for LLM Serving.” Abstract: “Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer, memory capacity and bandwidth increasingly become bottlenecks for serving performance. Agentic workloads compound this pressure through repeated interactions over growing contexts, making it increasingly