Beyond Model Speed: 19 Distributed Patterns to Optimize AI Latency
bibryam · x · 2026-09-01
Most AI latency optimization focuses on model speed, but AI applications are distributed systems where model inference is just one part of the critical path. This post outlines four categories of common distributed-systems patterns to reduce latency across the rest of the AI application:
- Locality Patterns: Includes colocation, replication, partitioning, and caching to bring data closer to requests.
- Work Reduction Patterns: Reduces computational load via algorithmic optimization, selective data processing, setup reuse, and request coalescing.
- Concurrent Execution Patterns: Improves throughput by avoiding synchronization, leveraging independent concurrency, progressive response, concurrency budgets, and hedged requests.
- Anticipation Patterns: Prepares work in advance using predictive prefetching, optimistic updates, speculative execution, precomputation, and prewarming.
The key is to trace the full request path, identify the dominant bottleneck, and apply the smallest pattern that resolves it.
Related event: 19 Distributed Patterns to Cut AI Application Latency(2 posts)→
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