Enterprise AI Analysis
Enhancing LLM Efficiency: Targeted Pruning for Prefill-Decode Disaggregation in Inference
Authors: Hao Zhang, Mengsi Lyu, Yulong Ao, Yonghua Lin
Affiliation: Beijing Academy of Artificial Intelligence
Executive Impact
Our innovative pruning method for Large Language Models delivers significant performance gains and cost reductions, making advanced AI more accessible and efficient for enterprise deployment.
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Inference Speedup
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Bandwidth Reduction
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Avg. Performance Score (PD Disaggregation)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview
Methodology
Experiments
Conclusion
Strategic LLM Pruning for Enhanced Efficiency
Large Language Models (LLMs) demonstrate exceptional capabilities across various tasks, but their deployment is constrained by high computational and memory costs. Model pruning provides an effective means to alleviate these demands. However, existing methods often ignore the characteristics of prefill-decode (PD) disaggregation in practice. Our novel pruning method for PD disaggregation inference enables more precise and efficient block and KV Cache pruning, achieving a 20.56% inference speedup and a 4.95× reduction in data transmission bandwidth consumption.
20.56%
Average Inference Speedup
4.95x
Data Transmission Bandwidth Reduction
Stage-Aware Pruning & KV Cache Optimization
We propose a novel pruning method for PD disaggregation inference, enabling more precise and efficient block and KV Cache pruning. Our approach constructs pruning and distillation sets to perform iterative block removal independently for the prefill and decode stages, obtaining better pruning solutions. We introduce a token-aware cache pruning mechanism that retains all KV Cache in the prefill stage but selectively reuses entries for the first and last token sequences in selected layers during decode, reducing communication costs with minimal overhead.
Enterprise Process Flow: Prefill-Decode Disaggregation with Pruning
Prefill Node Processes Prompts
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Distinct Block Removal (Pruning & Distillation) Applied
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KV Cache Generated (Full on Prefill)
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Selected KV Cache Transmitted to Decode
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Decode Node Processes Tokens with Pruned Model & KV Cache
| Feature | Our Method | Traditional Pruning |
|---|---|---|
| Approach to Prefill-Decode |
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| Block Pruning Strategy |
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| KV Cache Pruning |
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| Efficiency Focus |
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Robust Performance Across Models & Benchmarks
Extensive experiments demonstrate that our approach consistently achieves strong performance in both PD disaggregation and PD unified settings without disaggregation. Our method achieves a 20.56% inference speedup and a 4.95× reduction in data transmission bandwidth consumption, outperforming existing baselines across various LLMs and benchmarks including LLaMA3.1-8B, LLaMA2-13B, and Qwen2.5-7B.
| Method | Avg Score |
|---|---|
| Dense | 65.46 |
| LLM-Pruner | 56.07 |
| FLAP | 56.11 |
| Shortened | 53.66 |
| ShortGPT | 56.08 |
| SLEB | 48.42 |
| Ours | 62.99 |
| Method | DataVol (G) | Speed (µs) |
|---|---|---|
| Original | 4.0 | 11628 |
| Ours | 0.8 | 2347 |
| Strategy | Avg Score |
|---|---|
| Unified | 47.31 |
| Disaggregation (Ours) | 49.50 |
Qualitative Generation Performance
Despite substantial reduction in model size, our pruned model consistently generates coherent, information-rich, and well-structured responses, retaining the core semantics of the original output. This confirms strong generation capabilities and factual consistency.
Prompt: Explain what artificial intelligence is.
Original Output: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. Particular applications of AI include...
Pruned Output: Artificial intelligence, or AI, is a field of computer science that deals with the creation of machines that can perform tasks that normally require human intelligence. This includes things such as understanding natural language, recognizing objects in images, and making decisions based on complex data.
Summary & Future Directions
We proposed a pruning method deeply integrated with PD disaggregation, constructing pruning and distillation sets for iterative block removal independently tailored to prefill and decode stages, achieving superior solutions. Our token-aware KV Cache pruning mechanism reduces bandwidth usage with negligible overhead. Experimental results confirm strong performance in both PD disaggregation and PD unified settings. Future work includes pruning-aware memory management strategies and MoE pruning within the PD disaggregation framework, further enhancing efficiency.
Calculate Your Potential ROI
See how targeted LLM efficiency can translate into significant operational savings and reclaimed hours for your enterprise.
Estimated Annual Savings
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Total Hours Reclaimed Annually
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Your AI Implementation Roadmap
Our structured approach ensures a smooth integration of advanced LLM efficiency techniques into your existing enterprise infrastructure.
Phase 1: Discovery & Assessment
Comprehensive analysis of your current LLM deployment, identifying key optimization opportunities and assessing existing infrastructure for Prefill-Decode Disaggregation suitability.
Phase 2: Strategy & Customization
Develop a tailored pruning and KV Cache optimization strategy, leveraging our stage-aware block removal and token-aware cache pruning methods, customized to your specific models and workloads.
Phase 3: Implementation & Integration
Execute the pruning and optimization plan, seamlessly integrating the efficient LLM into your enterprise systems. Includes model fine-tuning and validation on your specific datasets.
Phase 4: Monitoring & Ongoing Optimization
Continuous performance monitoring, iterative refinement of pruning parameters, and adaptive adjustments to ensure sustained efficiency and optimal performance in dynamic environments.
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