Watt Counts: A Guide to Energy Efficiency Optimization for Large Language Models Under Heterogeneous GPU Architectures

Watt Counts is a guide project focused on energy efficiency optimization for Large Language Models (LLMs) under heterogeneous GPU architectures. It provides over 5000 experimental data points (covering 50 LLMs and 10 NVIDIA GPU models) and reveals the critical impact of hardware selection on energy efficiency. This project helps practitioners reduce energy consumption by 70% in server-side scenarios and 20% in batch processing scenarios, filling the gap in system-level energy-aware benchmarking and datasets.

Background: The Urgency of Energy Consumption Issues in Large Models

Energy consumption of Large Language Models (LLMs) has become a significant part of data center operating costs and carbon footprints. However, system operators lack clear guidance on energy-efficient deployment in heterogeneous hardware environments. The root cause is that existing benchmarks mostly focus on speed and accuracy, ignoring energy consumption measurement and optimization, making it difficult for users to select the optimal hardware combination for specific scenarios.

Watt Counts: Filling the Data Gap

Watt Counts is currently the largest open-source LLM energy consumption dataset, containing over 5000 experimental data points (50 LLMs, 10 NVIDIA GPU models) covering batch processing and online service scenarios. The team also provides a reproducible open-source benchmark framework, supports community submission of experimental results, and continuously expands the dataset coverage to keep up with the development of hardware and model ecosystems.

Energy Efficiency Characteristics of Heterogeneous GPU Architectures

Heterogeneous GPUs refer to the mixed use of GPUs from different generations and with different positioning. Their selection has a decisive impact on energy efficiency.

Batch Processing Scenarios

High-power flagship GPUs are not always optimal; mid-to-high-end GPUs may perform better due to a more favorable energy efficiency ratio. Matching memory capacity to model size is crucial to avoid increased energy consumption from memory swapping.

Online Service Scenarios

Latency, concurrency capability, and idle power consumption need to be considered comprehensively. Some GPUs with advanced manufacturing processes may not have outstanding peak performance, but their energy efficiency is better under actual loads. However, GPUs that perform well under high loads but have high idle power consumption may become a bottleneck.

Hardware-Aware Deployment Strategies

Core View: There is no universally optimal hardware; selection must be based on model characteristics and scenarios.

Model-Hardware Matching

Small models may not fully utilize high-end GPUs, leading to low energy efficiency. Ultra-large models need GPUs that match their memory bandwidth and capacity. Watt Counts data supports the evaluation of energy efficiency performance for different combinations.

Scenario-Driven Selection

Batch processing can use dynamic frequency adjustment and batch merging optimization; online services need to balance performance and power consumption. Hybrid deployment (sending latency-sensitive requests to fast GPUs and batch processing to energy-efficient GPUs) can improve overall energy efficiency.

Practical Guidance: Significantly Reducing Energy Consumption

Server-Side Scenarios

By selecting GPUs suitable for the model and load, combined with batch processing and scheduling strategies, energy consumption can be reduced by 70% without affecting user experience. The key is to understand load characteristics (request patterns, input/output lengths) and use Watt Counts data to evaluate configurations.

Batch Processing Scenarios

Optimizing GPU selection and task scheduling can reduce energy consumption by 20%. Although the percentage is not high, the absolute energy-saving effect is considerable because batch processing tasks have large data volumes and long running times.

Open-Source Ecosystem and Community Contributions

Watt Counts adopts an open-source model, with open datasets and tools: ensuring data transparency and verifiability; encouraging community contributions to expand the dataset; lowering the threshold for energy efficiency evaluation. The team calls on hardware manufacturers, cloud service providers, and model developers to participate, share data to improve methodologies, and promote the development of sustainable AI.

Conclusions and Outlook

Watt Counts reveals the energy efficiency laws of LLMs under heterogeneous GPUs through large-scale data, proving the critical impact of hardware selection. The guidance provided can achieve a 70% reduction in energy consumption for server-side scenarios and a 20% reduction for batch processing scenarios. In the future, the project will continue to track energy efficiency trends and evaluate new technologies to help achieve a win-win situation for AI and environmental protection.