Inception's Mercury 2 speeds around LLM latency bottleneck

"Mercury 2 is intended to solve a common LLM bottleneck involving autoregressive sequential decoding. The model instead generates responses through parallel refinement, a process that produces multiple tokens simultaneously and converges over a small number of steps, resulting in much faster generation and changes the reasoning trade-off."

"Higher intelligence typically leads to more computation at test time, meaning longer chains, more samples, and more retries. This all results in higher latency and costs. Mercury 2 uses diffusion-based reasoning to provide reasoning-grade quality inside real-time latency budgets."

Inception announced Mercury 2 on February 24 as the world's fastest reasoning LLM designed for production AI applications. The model addresses a fundamental bottleneck in traditional LLMs by replacing autoregressive sequential decoding with parallel refinement, which generates multiple tokens simultaneously and converges over a small number of steps. This approach significantly reduces latency and costs while maintaining reasoning-grade quality. Mercury 2 uses diffusion-based reasoning to deliver high-intelligence responses within real-time latency budgets, solving the typical trade-off where increased intelligence requires more computation, longer chains, and higher costs. Developers can request access through Inception's website or test the model via Inception chat.

Read at InfoWorld