AI BriefingTokenGate Enables 1.4-11.3x Faster LLM Prefill via Learned Token Pruning
·2026.10.21 20:49
Key point
A drop-in component for transformers that reduces peak memory by 1.5-3.4x while maintaining accuracy.
Details
- TokenGate is a small, drop-in component that adds end-to-end learnable token pruning to existing trained models for fast and memory-efficient prefill.
- Achieves 1.4-11.3x faster time-to-first token (TTFT) and 1.5-3.4x lower peak memory with no accuracy degradation across diverse tasks.
- Outperforms best-performing prior token-pruning methods by up to 6.4x in prefill speed and 2x in peak memory reduction.
- Uses token-gated attention, a Mixture-of-Experts-inspired mechanism, to re-weight attention logits using token importance scores, enabling learning despite the non-differentiable top-K operator.
- Natively extends standard transformers (e.g., Qwen3) and hybrid-attention models (e.g., Qwen3.5) with lightweight adaptation comparable to standard parameter-efficient fine-tuning.
- Code is available on GitHub and the paper is posted on arXiv.
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