PALS: Power-Aware LLM Serving for Mixture-of-Experts Models

2026-05-20 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

PALS, a novel power-aware runtime for large language model (LLM) serving, addresses the significant GPU energy consumption in modern data centers by treating GPU power caps as a controllable resource. Unlike prior systems that view power as a static constraint, PALS jointly optimizes power caps with software parameters such as batch size. The system integrates lightweight offline power-performance models with a feedback-driven controller to select configurations that meet throughput targets while maximizing energy efficiency. Implemented within the vLLM framework, PALS requires no model retraining or API changes. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS demonstrates up to 26.3% improvement in energy efficiency, reduces Quality of Service (QoS) violations by 4x to 7x under power constraints, and effectively tracks dynamic power budgets.

Key takeaway

For MLOps Engineers managing LLM inference workloads, PALS offers a critical approach to optimize energy consumption and performance. If you are struggling with GPU power constraints or QoS violations, consider integrating power control directly into your LLM serving framework. This can significantly improve energy efficiency by up to 26.3% and reduce QoS violations by 4x to 7x, enabling more sustainable and cost-effective AI deployments.

Key insights

PALS integrates GPU power control into LLM inference runtimes for energy-efficient, grid-interactive AI systems.

Principles

• GPU power caps are a controllable resource.
• Jointly optimize power caps with software parameters.

Method

PALS combines lightweight offline power-performance models with a feedback-driven controller to select configurations that satisfy throughput targets while maximizing energy efficiency.

In practice

• Integrate power control into LLM runtimes.
• Reduce QoS violations under power limits.

Topics

• LLM Serving
• GPU Power Management
• Mixture-of-Experts
• Energy Efficiency
• vLLM
• AI System Optimization

Best for: CTO, VP of Engineering/Data, Director of AI/ML, MLOps Engineer, AI Engineer, AI Architect

Related on AIssential

• PALS: Power-Aware LLM Serving for Mixture-of-Experts Models — PALS optimizes LLM serving energy efficiency by dynamically controlling GPU power caps and batch sizes using feedback-driven models.
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• Cost-Aware Model Orchestration for LLM-based Systems — Integrating quantitative performance and energy metrics into LLM orchestration significantly improves accuracy and efficiency.
• Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning — Dynamic, demand-aware cross-model memory coordination is crucial for cost-efficient multi-LLM serving with strict SLOs.
• microsoft / BitNet — bitnet.cpp enables fast, lossless, and energy-efficient 1-bit LLM inference on commodity hardware.
• Accelerating LLM Inference with Prompt Caching for Open‑Source Models on Databricks — Prompt caching for LLMs significantly reduces inference latency and increases throughput by reusing identical prompt prefixes.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.