The rapid advancement of Large Language Models (LLMs) has brought unprecedented capabilities to artificial intelligence, but these powerful models come with significant challenges in energy consumption and latency. As organizations increasingly deploy LLMs across a spectrum of hardware—from mobile edge devices to cloud GPU clusters—the need for precise profiling tools has become paramount. In response to this growing demand, researchers have developed ELANA, a simple yet powerful open-source tool designed to analyze the energy and performance characteristics of LLMs.

Article illustration 1

The Challenge of LLM Efficiency

LLMs represent one of the most computationally intensive workloads in modern AI. Their deployment efficiency is constrained by two primary factors: latency—the time it takes to generate responses—and power consumption—the energy required to process inputs and produce outputs. These constraints become particularly acute when deploying models on resource-constrained edge devices or optimizing for cost-effective cloud operations.

"The latency and power consumption of large language models (LLMs) are major constraints when serving them across a wide spectrum of hardware platforms, from mobile edge devices to cloud GPU clusters," state the researchers in their paper. "Benchmarking is crucial for optimizing efficiency in both model deployment and next-generation model development."

Introducing ELANA: A Comprehensive Profiling Solution

ELANA (Energy and Latency Analyzer) emerges as a response to these challenges, offering a lightweight, academic-friendly profiler that provides detailed insights into LLM performance. The tool is designed to measure several critical metrics:

  • Model size: The memory footprint of the neural network
  • Key-value (KV) cache size: Memory requirements for maintaining context during generation
  • Prefilling latency (Time-to-first-token, TTFT): Initial processing time before the first output token
  • Generation latency (Time-per-output-token, TPOT): Time required for each subsequent token
  • End-to-end latency (Time-to-last-token, TTLT): Total time from input to final output

What sets ELANA apart is its versatility. It supports all publicly available models on Hugging Face, making it immediately useful to researchers and developers working with the vast ecosystem of pre-trained models. The tool also features a simple command-line interface, lowering the barrier to entry for those who need quick profiling insights without complex setup.

Cross-Platform Compatibility and Extensibility

One of ELANA's most significant advantages is its ability to operate across different hardware environments. The tool is designed to work effectively on both multi-GPU systems in cloud environments and edge GPU platforms, providing consistent profiling methodologies regardless of the underlying hardware.

The researchers have ensured full compatibility with popular Hugging Face APIs, allowing for seamless integration into existing workflows. This compatibility extends to compressed or low bit-width models, making ELANA particularly valuable for research into model optimization and efficiency improvements.

"Moreover, ELANA is fully compatible with popular Hugging Face APIs and can be easily customized or adapted to compressed or low bit-width models, making it ideal for research on efficient LLMs or for small-scale proof-of-concept studies," the authors explain.

Energy Consumption Monitoring

Beyond traditional performance metrics, ELANA offers optional energy consumption logging—a feature increasingly important as organizations face growing pressure to reduce their carbon footprint and operational costs. By providing visibility into the energy profile of different models and hardware configurations, ELANA enables data-driven decisions about model selection and deployment strategies.

The Broader Impact on AI Development

The release of ELANA arrives at a critical juncture in AI development. As models grow larger and more complex, the efficiency of their deployment has become as important as their capabilities. Tools like ELANA provide the quantitative data needed to make informed decisions about model optimization, hardware selection, and deployment strategies.

For researchers focused on developing more efficient models, ELANA offers a standardized methodology for comparing different approaches to model compression, quantization, and architecture optimization. For practitioners deploying these models in production, the tool provides insights needed to balance performance requirements with resource constraints.

Accessibility and Future Directions

The open-source nature of ELANA ensures that it will be accessible to researchers, developers, and organizations regardless of their resources. The researchers have made the tool available through their official repository, inviting contributions and improvements from the broader community.

Looking ahead, the development team suggests several potential enhancements, including integration with more specialized hardware profiling tools and expansion to support additional model architectures beyond those currently available on Hugging Face.

As the field of efficient AI continues to evolve, tools like ELANA will play an increasingly vital role in bridging the gap between theoretical model capabilities and practical deployment realities. By providing clear, actionable insights into the performance characteristics of LLMs, ELANA represents a significant contribution to the growing toolkit for responsible and efficient AI development.

This article is based on the research paper "ELANA: A Simple Energy and Latency Analyzer for LLMs" by Hung-Yueh Chiang, Bokun Wang, and Diana Marculescu, submitted to arXiv on December 7, 2025 (arXiv:2512.09946). The full paper and the ELANA tool are available at the official repository linked in the original publication.