Jet-QSparse speeds agentic LLM inference with 4-bit sparse attention

Researchers have introduced Jet-QSparse, a training-free method designed to accelerate agentic large language model inference using compact NVFP4 cache storage and native FP4 query-key computation on Blackwell tensor cores. The approach combines dense scanning with sparse value aggregation to preserve output precision while enabling contiguous tile loads. Evaluations across four hybrid LLMs show no detected accuracy loss on complex reasoning and coding benchmarks while achieving significant end-to-end decode speedups and expanded token capacity. Open article →