A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...
The ability to significantly reduce LLM decoding latency could lead to reduced computational resource requirements, making these powerful AI models more accessible and affordable to a wider range of ...
Speculative decoding accelerates large language model generation by allowing multiple tokens to be drafted swiftly by a lightweight model before being verified by a larger, more powerful one. This ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Jinsong Yu shares deep architectural insights ...
“LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequential nature of ...
This figure shows an overview of SPECTRA and compares its functionality with other training-free state-of-the-art approaches across a range of applications. SPECTRA comprises two main modules, namely ...
Reed–Solomon codes have long been celebrated for their robust error-correction capabilities in digital communications and data storage. Decoding algorithms for these codes continue to evolve, aiming ...
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