Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster

Researchers from Stanford and UC Berkeley released TokenGate, a drop-in learned token pruning module for large language models that 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. The module applies token-gated attention to prune tokens during prefill, targeting the cost of processing long inputs before generation begins.

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. The post Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster https://www.marktechpost.com/2026/10/25/stanford-berkeley-tokengate-learned-token-pruning-llm-prefill/ appeared first on MarkTechPost https://www.marktechpost.com .