Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster
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Stanford and Berkeley Researchers Build TokenGate to Cut LLM Prefill Costs
Liana Patel, Rishabh Ranjan, Ion Stoica, Matei Zaharia and Carlos Guestrin describe TokenGate, which adds a trainable gate that scores each token and keeps only the most important ones. The paper reports 1.5x to 3.4x lower peak memory during prefill, and the code is public on GitHub.
TokenGate Speeds LLM Prefill, But 11x Is Not A Planning Number » Saipien
Most of the wait on a long prompt happens before the model types a word. Give an AI agent a contract pack, a sales or support archive, or the notes it gathered before it acts, and the clock that matters is time-to-first-token: how long prefill takes to read the prompt and produce the first answer […]
Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster
Stanford and UC Berkeley researchers released TokenGate, a drop-in learned token pruning module for LLMs. It cuts long-context prefill time-to-first-token by 1.4x to 11.3x and peak memory by 1.5x to 3.4x without accuracy loss.
Researchers from Stanford and UC Berkeley presented TokenGate, a technique that allows language models to filter tokens before processing an extensive context. In their tests, the method accelerated time to the first token up to 11.3 times and reduced maximum memory usage, although the results varied according to the task and budget of tokens.
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