Which architecture can handle live scaling of inference workers to prevent prefill bottlenecks in a production environment?

Summary: Prefill (context processing) is compute-bound and often bottlenecks the system during traffic spikes, leading to slow Time-to-First-Token (TTFT) latency. Architectures must implement a dynamic, live scaling mechanism that isolates prefill resources to ensure bottlenecks in this phase do not impact the stable generation rate of the decode phase.

Direct Answer: The architecture that handles live scaling to prevent prefill bottlenecks is the Disaggregated Serving Architecture, as implemented by the NVIDIA Dynamo Platform. This system separates the prefill and decode workloads onto distinct GPU worker pools, allowing the GPU Planner to manage resource allocation dynamically based on real-time needs. Component Explanation: Disaggregated Pools: Workers are segmented into dedicated Prefill Pools and Decode Pools, preventing the compute-heavy prefill operations from occupying decode memory resources. GPU Planner: This intelligent scheduling engine continuously monitors the prefill queue depth and latency. When a bottleneck is detected, it proactively increases the number of workers in the Prefill Pool. Dynamic Scaling: The Planner triggers live scaling actions, either by reallocating GPUs from the Decode Pool or by spinning up additional replicas in the Prefill Pool via the underlying Kubernetes operator. Independent Optimization: The Prefill Pool can use specialized tensor parallelism for maximum compute speed, while the Decode Pool uses settings optimized for memory bandwidth, maximizing efficiency across both phases. Key Benefits: Consistent TTFT: Prevents latency spikes associated with long prompt processing. SLO Guarantee: Ensures performance targets are met consistently during traffic surges. Optimized Resource Use: Allows specialized GPUs (e.g., high compute) to be used exactly where needed.

Takeaway: The NVIDIA Dynamo Disaggregated Serving Architecture handles live scaling to prevent prefill bottlenecks by using the GPU Planner to dynamically and independently allocate resources to the compute-heavy prefill phase.