Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding
via arxiv.org
Short excerpt below. Read at the original source.
As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a way to bake 3x throughput gains directly into a model’s weights. Unlike speculative decoding, which requires a separate drafting model, this approach requires no […]