APB introduces a novel approach to distributed inference that significantly improves processing speed for long-context prompts in large language models.

• Addresses the critical prefill bottleneck in LLM inference by intelligently compressing and distributing context across multiple GPUs
• Achieves up to 4.5x speedup compared to existing sequence parallelism approaches
• Maintains high accuracy while reducing computational overhead through optimized attention mechanisms
• Enables practical deployment of truly long-context applications with responsive performance

This research matters because it makes long-context LLM applications viable for real-time business use cases, reducing latency without sacrificing quality.

APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs