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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors
TL;DR
HYMELL, a hybrid framework combining analytical modeling and machine learning, accurately estimates inference latency and energy consumption for large language models (LLMs). It achieves less than 5% error in predictions, enabling efficient design and optimization.
✦ Why It Matters
Implement HYMELL to optimize your LLM designs for latency and energy efficiency today.
Key Takeaways
- Utilize HYMELL for accurate latency and energy predictions in LLMs.
- Explore design space rapidly without hardware dependencies.
- Achieve less than 5% prediction error for LLaMA 3 8B.
- Optimize LLMs for energy efficiency and computational cost.
How It Works
HYMELL operates through a three-level hierarchy: it first uses analytical methods to estimate basic operations, then applies machine learning to predict more complex components, and finally integrates these predictions into an end-to-end model that captures system-level overheads during both the prefill and decode phases.