Intel and Israel’s Weizmann Institute of Science have unveiled a significant advance in AI performance through a new technique called speculative decoding, which dramatically speeds up large language model (LLM) processing.
The collaborative research, published in a recent paper, introduces the concept of “lookahead decoding,” a method that enables faster AI inference without compromising accuracy. According to Intel Labs, the breakthrough reduces inference latency by a factor of up to 2.3x and boosts throughput by up to 2x on Intel’s AI hardware.
“This joint research underscores our commitment to pushing the boundaries of AI performance,” said Gadi Singer, Vice President and Director of Emergent AI Research at Intel Labs. “By using speculative decoding, we’re able to anticipate future tokens and validate them efficiently, resulting in major speed improvements.”
Speculative decoding works by predicting multiple possible outputs (tokens) and validating them using a smaller, faster model before passing them to a larger, more complex model. This not only speeds up the process but also reduces computational load.
Professor Shimon Ullman from the Weizmann Institute emphasized the broader implications: “Our goal was to enhance AI’s practicality in real-world applications, especially where latency is a concern—like chatbots, assistants, or language translation tools.”
The innovation supports Intel’s broader mission to develop more efficient and scalable AI systems. It could have a wide-reaching impact across industries, from enterprise AI solutions to consumer applications.
The research will be presented at a leading AI conference later this year, showcasing how hardware-software co-design and collaborative research can lead to transformative advances in artificial intelligence.
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