---
title: "TokenGate prunes tokens to speed long-context prefill up to 11.3x"
url: https://www.parallelquant.com/posts/tokengate-prunes-tokens-to-speed-long-context-prefill-up-to-11-3x-022d24
source_name: "MarkTechPost"
source_url: https://www.marktechpost.com/2026/10/25/stanford-berkeley-tokengate-learned-token-pruning-llm-prefill/
published: 2026-10-25T12:12:18.000Z
topics: ["research", "llms"]
publisher: "Parallel Quant"
---

# TokenGate prunes tokens to speed long-context prefill up to 11.3x

*2026-10-25 · Source: [MarkTechPost](https://www.marktechpost.com/2026/10/25/stanford-berkeley-tokengate-learned-token-pruning-llm-prefill/)*

Stanford and UC Berkeley researchers released TokenGate, a drop-in learned token-pruning module for LLMs. It cuts time-to-first-token for long prompts by 1.4x to 11.3x and peak memory by 1.5x to 3.4x. The authors report no accuracy loss.

**Why it matters:** Prefill is the dominant latency and memory cost for agents that load large codebases or documents, so gains here directly lower serving cost. If the no-loss claim holds across models, it complements harness-level efficiency work by cutting cost at the attention layer itself.

**Topics:** research, llms

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Read the original: https://www.marktechpost.com/2026/10/25/stanford-berkeley-tokengate-learned-token-pruning-llm-prefill/
Canonical: https://www.parallelquant.com/posts/tokengate-prunes-tokens-to-speed-long-context-prefill-up-to-11-3x-022d24
Published by Parallel Quant — https://www.parallelquant.com