Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding
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 additional infrastructure — just a single special token added to the model's existing architecture.The limits of next-token pred...
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