TETRIS revolutionizes batch speculative decoding by optimizing draft token selection across multiple requests, significantly improving inference throughput for large language models.

• Intelligently selects the most promising draft tokens for parallel verification
• Reduces wasted computing resources by minimizing rejected tokens
• Achieves optimal resource utilization across batched requests
• Delivers faster inference without compromising output quality

This engineering breakthrough matters because it directly addresses one of the key challenges in LLM deployment: achieving maximum throughput when serving multiple users simultaneously, without requiring hardware upgrades.

TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding