Machine Learning LLM & Text Generation5 min reading time

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

MarkTechPostPublished 5 hours ago

Stanford and UC Berkeley researchers introduced TokenGate, a module that enables learned token pruning in large language models to speed up long-context prefill. TokenGate achieves up to 11.3x faster time-to-first-token and reduces memory usage without accuracy loss, tested on Qwen3 models with contexts up to 128k tokens.

#long-context-language-models #qwen3-1-7b #qwen3-5-2b #stanford #tokengate #uc-berkeley

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

More in Machine Learning

Google ML Drift Accelerates On-Device AI Inference Across Diverse GPUs

Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend

AI21 Labs Releases LIFT-Bench to Curb Reward Hacking in RL Training