Leveraging KV Similarity for Online Structured Pruning in LLMs
AI-Powered LLM Pruning: Accelerate Inference, Preserve Accuracy
Discover how Token Filtering's novel KV Similarity approach delivers significant speedups for Large Language Models without compromising performance.
Token Filtering redefines LLM inference efficiency, offering unprecedented speed and accuracy preservation.
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% Pruning Achieved
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% Latency Reduction
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% Memory Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Token Filtering introduces a lightweight online structured pruning technique that leverages joint key-value similarity to identify and skip redundant attention computations.
Token Filtering Process Flow
Tokens Pass Through Filtering Layer
→
Compute Key/Value Similarity
→
Anchor Key/Value Averaging
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Similarity Score Calculation
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Skip Attention Layer (if score is high)
+8.49 points
Accuracy Gain (50% Pruning vs. Baseline)
| Feature | Offline Pruning | Online Pruning (Token Filtering) |
|---|---|---|
| Calibration Data | Required |
|
| Generalization | Limited |
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| Dynamic Adaptation | No |
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| Runtime Overhead | Low (once applied) |
|
Token Filtering demonstrates robust performance across various LLM models and pruning ratios, significantly outperforming prior methods in terms of perplexity and accuracy.
65.90%
Avg. Accuracy on LLaMA-2-13B (50% Pruning)
LLaMA-2-13B Benchmarking (50% Pruning)
On LLaMA-2-13B, Token Filtering achieves a comparable perplexity (29.22 vs. 28.86) and a substantial accuracy gain of 8.49 points (65.90 vs. 57.41) over the best baseline, even at 50% pruning. This demonstrates strong robustness and effective preservation of representational capacity.
70.52%
Avg. Accuracy on Phi-4-14B (20% Pruning)
The online, tail-focused pruning strategy, combined with KV similarity, allows Token Filtering to achieve significant latency and memory reductions, especially for large batch sizes.
97%
% Attention Share in Total Latency (Batch 128)
Latency Reduction at Scale
At a batch size of 128, Token Filtering reduces latency by 46.6% and memory usage by 33.6%. This is crucial as attention operations account for nearly all latency in large batches (up to 97%), making direct pruning of attention layers highly impactful.
| Strategy | PPL (Lower is better) | Avg. Accuracy (Higher is better) |
|---|---|---|
| Uniform Pruning | 429.44 | 39.60% |
| Head-focused Pruning | 6717.69 | 34.78% |
| Tail-focused Pruning (Token Filtering) | 29.22 |
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Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings Token Filtering can bring to your enterprise LLM operations. Adjust parameters to see the immediate impact.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our structured approach ensures a seamless integration of Token Filtering into your existing LLM infrastructure, minimizing disruption and maximizing ROI.
Phase 1: Discovery & Strategy
We begin with a comprehensive analysis of your current LLM usage, identifying key areas for optimization and defining measurable objectives.
Phase 2: Customization & Integration
Tailoring Token Filtering to your specific models and workflows, followed by seamless integration into your inference pipeline.
Phase 3: Performance Validation & Scaling
Rigorous testing and validation of performance gains, ensuring stability and preparing for enterprise-wide deployment.
Phase 4: Ongoing Support & Optimization
Continuous monitoring, support, and further optimization to adapt to evolving model architectures and business needs.
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