Making LLMs faster without sacrificing accuracy

Jul 10, 2026

Amazon Science researchers have introduced a new scaling law that connects specific architectural choices in large language models (LLMs) to their loss, allowing for the identification of models that can improve throughput by up to 47% without any loss of accuracy. This approach enables more efficient LLM inference while maintaining performance.

Why it matters: This scaling law provides a systematic method to accelerate LLM inference, potentially reducing costs and latency in production systems without sacrificing accuracy.

Full story at: Amazon Science ↗