3 sources
An important systems page because he is one of the named authors on speculative decoding, a technique that became part of the mainstream conversation about making large-model inference materially faster without changing outputs.
Researcher Profile
Editor reviewedYaniv Leviathan
Faster LLM inference via speculative decoding
Google Fellow focused on fast and practical generative AI systems
A high-signal researcher for the latency and systems side of modern language models, especially where clever decoding tricks turn frontier models into usable products.
Organizations
Google
Topics
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Speculative decoding
02
Fast inference for large language models
03
Practical generative-systems work at Google
04
Faster LLM inference via speculative decoding
05
Fast Inference from Transformers via Speculative Decoding
06
Inference
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
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Important because his profile sits at the intersection of field-level research leadership and concrete systems work such as speculative decoding that directly changed how modern LLM inference gets deployed.
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Co-authored DeepSpeed Inference: practical inference optimizations for serving large transformer models.
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Co-authored DeepSpeed Inference: practical inference optimizations for serving large transformer models.