Watt Counts: Energy-Aware Benchmark for Sustainable LLM Inference on Heterogeneous GPU Architectures
Watt Counts optimizes LLM energy efficiency on heterogeneous GPUs using a dataset of 5,000+ experiments, achieving up to 70% energy savings.
Key Findings
Methodology
The study uses the Watt Counts dataset, which includes energy consumption data for 50 LLMs across 10 NVIDIA GPUs, to conduct a system-level analysis. A customizable open-source benchmark tool is used to study the impact of different GPU architectures on energy efficiency, providing hardware-aware deployment guidance.
Key Results
- In server scenarios, energy consumption can be reduced by 70% with negligible impact on user experience by selecting appropriate GPUs.
- In batch scenarios, energy consumption can be reduced by 20%, indicating significant impact of hardware choice on energy efficiency.
- Experiments show that GPU architectural features like memory bandwidth and cache hierarchy significantly affect energy efficiency.
Significance
This study provides empirical data and tools for optimizing LLM deployment on heterogeneous GPUs, filling a gap in energy benchmarking. The data-driven approach significantly reduces energy consumption, supporting sustainable AI development.
Technical Contribution
The study offers the first large-scale open dataset covering various GPU architectures and LLM models for energy efficiency analysis. The proposed benchmark tool requires no external power meters, is easily reproducible and extendable, and provides new possibilities for hardware-aware LLM deployment.
Novelty
This is the first systematic analysis of LLM energy efficiency on heterogeneous GPUs, providing a comprehensive empirical dataset and open-source tools, filling a gap in existing research.
Limitations
- Experiments are limited to single-GPU setups, not considering multi-GPU communication overhead.
- The dataset is primarily based on NVIDIA GPUs, which may not be applicable to other brands.
Future Work
Future research could expand to multi-GPU setups, explore energy efficiency across different GPU brands, and develop more complex energy optimization algorithms.
AI Executive Summary
The high energy consumption of large language models (LLMs) is a significant challenge in AI, particularly in inference deployments on heterogeneous GPU architectures. Existing solutions lack energy-efficient benchmarks and data, making it difficult for system operators to make informed hardware choices.
Watt Counts fills this gap by providing an open dataset of over 5,000 experiments covering the energy performance of 50 LLMs on 10 NVIDIA GPUs. The study shows that GPU selection is crucial for energy efficiency outcomes, with optimal hardware choices varying across models and deployment scenarios.
Guided by data and insights, practitioners can reduce energy consumption by up to 70% in server scenarios and 20% in batch scenarios. This research provides a data-driven foundation for sustainable LLM system deployment.
Deep Analysis
Background
With the widespread use of large language models, understanding and optimizing their energy consumption has become a critical research topic. While training phases are typically more energy-intensive, inference phases often have a greater total energy impact due to the need for continuous server deployments. Existing research primarily focuses on training, lacking systematic analysis of inference energy consumption.
Core Problem
The energy consumption problem of LLM inference deployments is particularly complex in heterogeneous GPU architectures. The lack of energy-efficient benchmarks and data makes it challenging for system operators to make informed hardware choices and find the optimal balance between energy efficiency and performance.
Innovation
Watt Counts provides the first large-scale open dataset covering various GPU architectures and LLM models for energy efficiency analysis. The proposed benchmark tool requires no external power meters, is easily reproducible and extendable, and provides new possibilities for hardware-aware LLM deployment.
Methodology
- �� Designed an experimental framework involving 50 LLMs and 10 GPUs.
- �� Used the vLLM engine for inference, measuring energy and performance metrics.
- �� Conducted energy efficiency analysis in batch and server scenarios using a customizable open-source benchmark tool.
Experiments
Experiments used the SQuAD v1.1 dataset, covering batch and server scenarios. By comparing the energy efficiency of different GPU architectures, the study analyzed the impact of factors like memory bandwidth and cache hierarchy on energy efficiency.
Results
In server scenarios, energy consumption can be reduced by 70% with negligible impact on user experience by selecting appropriate GPUs. In batch scenarios, energy consumption can be reduced by 20%, indicating significant impact of hardware choice on energy efficiency.
Applications
The study's findings can be used to optimize LLM deployments in data centers and cloud computing environments, reducing energy consumption and operational costs, supporting sustainable AI development.
Limitations & Outlook
Experiments are limited to single-GPU setups, not considering multi-GPU communication overhead. The dataset is primarily based on NVIDIA GPUs, which may not be applicable to other brands.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen with many different appliances. Each appliance has different energy consumption and efficiency. You need to choose the right appliance for each cooking task to save on electricity and time. This study is like helping you choose the best appliance to complete tasks with optimal energy efficiency.
ELI14 Explained like you're 14
Imagine you're playing a game where the goal is to complete tasks using the least amount of energy. You have many different characters, each with different skills and energy consumption. You need to choose the best character for each task to save energy. This study is like helping you pick the best character to complete tasks with optimal energy efficiency!
Glossary
Large Language Model (LLM)
A type of AI model trained on large datasets to understand and generate natural language.
The study analyzes LLM energy consumption across different GPUs.
Heterogeneous GPU Architecture
A computing system composed of different types of GPUs, each with varying performance and energy characteristics.
The study examines the impact of heterogeneous GPU architectures on LLM energy efficiency.
Energy Efficiency
The ratio of energy consumed to the performance achieved when completing a specific task.
The main goal of the study is to optimize the energy efficiency of LLM inference.
Benchmarking
A standardized testing method used to evaluate system performance and energy consumption.
The study uses a custom benchmarking tool to measure energy consumption.
SQuAD v1.1
A dataset used for training and evaluating question-answering systems, containing question-answer pairs based on Wikipedia articles.
The experiments used the SQuAD v1.1 dataset to evaluate LLM performance.
Open Questions Unanswered questions from this research
- 1 How to optimize energy efficiency in multi-GPU setups remains an open question, as existing research focuses on single-GPU setups.
- 2 The energy efficiency performance of different GPU brands in LLM inference is not yet fully explored.
Applications
Immediate Applications
Data Center Optimization
By selecting appropriate GPUs, data centers can significantly reduce energy consumption and operational costs.
Long-term Vision
Sustainable AI Development
By optimizing LLM energy efficiency, AI technology can be driven towards more sustainable development, reducing carbon emissions.
Abstract
While the large energy consumption of Large Language Models (LLMs) is recognized by the community, system operators lack guidance for energy-efficient LLM inference deployments that leverage energy trade-offs of heterogeneous hardware due to a lack of energy-aware benchmarks and data. In this work we address this gap with Watt Counts: the largest open-access dataset of energy consumption of LLMs, with over 5,000 experiments for 50 LLMs across 10 NVIDIA Graphics Processing Units (GPUs) in batch and server scenarios along with a reproducible, open-source benchmark that enables community submissions to expand this dataset. Leveraging this dataset, we conduct a system-level study of LLM inference across heterogeneous GPU architectures and show that GPU selection is crucial for energy efficiency outcomes and that optimal hardware choices vary significantly across models and deployment scenarios, demonstrating the critical importance of hardware-aware deployment in heterogeneous LLM systems. Guided by our data and insights, we show that practitioners can reduce energy consumption by up to 70% in server scenarios with negligible impact on user experience, and by up to 20% in batch scenarios.