FrontierThe story, in brief
Making LLMs faster without sacrificing accuracy
47%. That's how much faster new LLMs can run without sacrificing a single point of accuracy.
The KeyNews take
Why it matters
A new scaling law from Amazon Science reveals architectural choices that unlock significant throughput improvements—critical for cost-competitive inference at scale. This directly impacts model efficiency economics for builders deploying at production scale.
The key facts
5 to knowNew scaling law correlates architectural choices to loss function
Up to 47% throughput improvement with no accuracy loss
Source: Amazon Science research
Published: May 15, 2026
Focus on inference optimization without capability tradeoff
Go to the source
Amazon Scienceamazon.science
Publisher excerpt: A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.