HELIOS introduces a novel dynamic inference serving system that balances accuracy, latency, and throughput for large language models.

• Implements early-exit strategies allowing models to skip unnecessary computation when confident about outputs
• Employs adaptive model selection to choose the most suitable model for each request
• Significantly reduces inference latency while maintaining accuracy
• Achieves up to 3.2x throughput improvement compared to traditional approaches

This research addresses critical engineering challenges in LLM deployment, enabling organizations to efficiently scale AI services while optimizing resource utilization and performance.

HELIOS: Adaptive Model And Early-Exit Selection for Efficient LLM Inference Serving