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 module for long-context prefill.TokenGate learned token pruning lets an existing LLM skim its input.Only the tokens that matter get full processing.The research team reports 1.4x to 11.

3x faster time-to-first-token (TTFT) with no accuracy loss.Peak prefill memory drops 1.5x to 3.4x.Code and checkpoints are on GitHub under Apache-2.0.TL;DR MLP gate: A small MLP gate and LoRA (r=16) added to Qwen3-1.7B or Qwen3.5-2B.Tested on contexts up to 128k tokens.

Runs on: Paper timings use 1 A100 at batch size 1.Training used 4 A100s.Performance: Pareto-optimal on 5 tasks against 6 baselines.Best: NIAH 16k at 11.3x faster TTFT and 3.42x lower memory.TTFT gain hits 13.0x at 128k.Worst: Speedup drops to 1.4x on some tasks.What is TokenGate?

TokenGate is a drop-in component that adds trainable token pruning to pretrained LLMs.Transformers push every token through every layer.Attention cost grows quadratically with length.Training-free pruning is cheap but loses accuracy at high pruning rates.

Architectural changes often need pre-training from scratch.How does TokenGate learned token pruning work?The forward pass splits into 3 phases: Standard forward: layers below gate layer L process all N tokens as usual.Gate module: an MLP scores each token from its post-attention residual at layer L.

A top-K selector keeps the highest-scoring tokens.Pruned forward: layers above L process only those K tokens and never create KV entries for pruned ones.Only the document part of the prompt is prunable.The question is never pruned.What is token-gated attention?

Top-K selection is not differentiable, which blocks end-to-end training.TokenGate borrows the Mixture-of-Experts router trick.Each kept token’s score becomes a bias on attention logits above the gate.That bias gives gradients a path back to the scorer.

A learnable per-head gain and a learnable temperature refine this.The temperature corrects for each query seeing fewer keys.Training uses plain next-token loss.Each step averages a full-context loss and a random-budget loss.So 1 trained gate works across budgets.window.

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important} How does TokenGate perform on benchmarks?The research team tested Qwen3-1.7B on RepoBench-P, 2WikiMultihopQA, MultiNews, BioDEX and needle-in-a-haystack (NIAH).Baselines were FastKV, SlimInfer, DASH, GemFilter, FlexPrefill and MInference, on the same LoRA backbone.Versus full prefill: 1.

4x to 11.3x faster TTFT and 1.5x to 3.4x lower memory.Versus the next-best baseline: 1.2x to 6.4x faster TTFT and up to 2.1x lower memory.2WikiMultihopQA: at 95% of full-context accuracy, prefill runs 3.0x faster with 2.7x less memory.Longer contexts: a gate trained at 16k reaches 12.

8x at 32k and 13.0x at 64k and 128k.Memory savings stay near 3.43x.The learned selector beats FastKV’s heuristic at the same layer.Even random-K selection scores higher on the TokenGate backbone.The research team credits backbone co-adaptation.Does it work on hybrid-attention models?Yes, Qwen3.

5-2B mixes Gated DeltaNet and full-attention layers.On NIAH 32k, TokenGate at layer 3 matched full-prefill accuracy.It ran 3.8x faster than full prefill and 2.1x faster than FastKV.Gating at layers 1 or 2 reached 7.8x, within 6% accuracy.FastKV can only prune attention layers.

TokenGate vs FastKV, SlimInfer, GemFilter and DASH FeatureTokenGateFastKVSlimInferGemFilterDASHToken selectionLearned MLP gateAttention from last queriesBlock attention, multi-stageLast-query attention at filter layerHidden-state stabilityTraining neededYes (gate + LoRA r=16)NoNoNoNoPruning layer in TokenGate’s Qwen3-1.

7B runs113 (default)8, 17, 251211Prunes at Gated DeltaNet layersYesNo (attention layers only)Not disclosedNot disclosedNot disclosedBest self-reported prefill gain (own setup)11.3x TTFT, 3.4x memory1.82x prefill2.53x TTFT (LLaMA3.1-8B, RTX 4090)2.

4x speed, 30% less GPU memory vs SOTANot disclosedCodeGitHubGitHubGitHubGitHubAnnounced Self-reported gains use different setups, so they are not directly comparable.Pruning layers come from TokenGate’s Appendix C.2.How do you deploy TokenGate?The GitHub Repo needs Python 3.10+ and CUDA PyTorch 2.

9+.An optional FlashAttention-2 key-bias kernel speeds up dense Qwen3.It costs 1.0x to 1.1x a plain call.Without it, inference falls back to FlexAttention.A {state} placeholder marks the prunable document.generate takes a maxkeptprefill_tokens budget.Checkpoints cover all 5 tasks on Qwen3-1.

7B, plus a Qwen3.5-2B NIAH gate.Example: liana313/tokengate-niah-qwen3-1.7b.Training mirrors a TRL/PEFT recipe.Each step is 1.3x slower than plain LoRA.Runs converged faster on 4 of 5 tasks.Mean training time was 1.8 versus 2.2 A100 hours.

Key Takeaways TokenGate adds learned token pruning to existing LLMs via a small gate and LoRA.Prefill runs 1.4x to 11.3x faster with 1.5x to 3.4x lower peak memory.Token-gated attention makes top-K selection trainable, MoE-router style.It beats the next-best pruning baseline by up to 6.4x on TTFT.

It works on hybrid Qwen3.5-2B, where FastKV cannot prune DeltaNet layers.Check out the Paper, GitHub repo and Hugging Face paper page.Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter.Wait!are you on telegram?

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3x Faster appeared first on MarkTechPost.