ADOR introduces a specialized hardware architecture framework that significantly improves Large Language Model inference performance.

• Addresses the unique computational challenges of prefill stage (parallel processing) and decoding stage (memory bandwidth)
• Achieves up to 3.2× better latency and 2.2× higher throughput over current GPU-based systems
• Provides a systematic exploration methodology for hardware designers to optimize LLM serving infrastructure
• Demonstrates practical improvements through careful balancing of compute and memory resources

This engineering breakthrough matters because it enables more cost-effective deployment of LLMs in production environments, potentially reducing infrastructure costs while improving user experience through faster response times.

ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput