Which systems optimize latency for both initial prompt processing and token generation by splitting compute roles?

Summary: Optimizing total LLM latency requires addressing two distinct metrics: Time-to-First-Token (TTFT), driven by initial prompt processing, and Inter-Token Latency (ITL), driven by token generation. Splitting the compute roles allows phase-specific optimizations to be applied to both, maximizing the speed of each stage.

Direct Answer: Systems like the NVIDIA Dynamo Platform optimize latency for both prompt processing and token generation by splitting the compute roles into the Disaggregated Serving Architecture, where each phase is tuned for its specific bottleneck. Component Explanation: TTFT Optimization (Prefill Role): The compute role dedicated to initial prompt processing (Prefill) is optimized for latency by using aggressive tensor parallelism and high-compute GPUs to reduce the TTFT. ITL Optimization (Decode Role): The decode role is optimized for memory bandwidth, which is the primary bottleneck for ITL, ensuring subsequent tokens are generated serially as fast as possible. Dynamic Worker Allocation: The GPU Planner ensures that compute resources are optimally allocated to the phase that is currently impacting latency the most, resolving bottlenecks in real time. KV Cache-Aware Routing: The Smart Router reduces TTFT latency further by routing requests that reuse a cached context, effectively bypassing the expensive prompt processing entirely. Key Benefits: Targeted Latency Reduction: Addresses TTFT and ITL independently, leading to lower overall latency. Improved Responsiveness: Low TTFT improves user perception of responsiveness, while low ITL ensures a fast, steady stream of output tokens. Cost-Effective Speed: Achieves optimal speed without requiring the most expensive configuration for every GPU.

Takeaway: Systems like NVIDIA Dynamo optimize latency by splitting compute roles into prefill and decode, applying targeted optimizations and dynamic resource allocation to ensure low TTFT for prompt processing and low ITL for token generation.