# Making LLMs faster without sacrificing accuracy

DevFeed: [Making LLMs faster without sacrificing accuracy](<https://devfeed.tech/articles/making-llms-faster-without-sacrificing-accuracy-7603.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/making-llms-faster-without-sacrificing-accuracy>)

Author: Tao Yu; Youngsuk Park

Published: 2026-05-15T13:00:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [chinchilla-scaling-law](<https://devfeed.tech/tags/chinchilla-scaling-law.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [grouped-query-attention](<https://devfeed.tech/tags/grouped-query-attention.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-efficiency](<https://devfeed.tech/tags/inference-efficiency.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm-optimization](<https://devfeed.tech/tags/llm-optimization.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [network-architectures](<https://devfeed.tech/tags/network-architectures.md>), [scaling-laws](<https://devfeed.tech/tags/scaling-laws.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

## AI overview

The article presents scaling laws that connect LLM architectural choices to the tradeoff between accuracy and efficiency. It describes how these choices can improve inference throughput without reducing accuracy.

## Source 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.