The traditional autoregressive nature of Large Language Models, generating one token at a time, creates a massive bottleneck for production workloads, heavily constrained by memory bandwidth. As models grow, scaling inference while maintaining low latency becomes one of the most complex engineering challenges.

Gemma 4 solves this bottleneck head-on by integrating native MTP drafters, revolutionizing the concept of speculative decoding. Instead of relying on a separate, smaller draft model with the associated alignment overhead, it is architected to predict multiple future tokens simultaneously, drastically accelerating generation speed without sacrificing output quality.

In this session, we will explore the mechanics of MTP drafters and we will dissect how this architecture optimizes compute resources, maximizes memory bandwidth utilization, and redefines performance benchmarks for open-weights models.