LLM production workloads are non-stationary and bursty. To optimize, move away from synthetic benchmarks, implement token-aware load balancing, and prioritize KV cache strategies that target shared prompt prefixes rather than full request-response pairs.
Learn how LLM serving workloads evolve over time. Groundwork provides an evidence-based analysis of production traces, caching, and load-balancing strategies.
Synthetic benchmarks fail to capture the bursty, non-stationary nature of real-world traffic. Production data shows that request arrival patterns and computational intensity fluctuate significantly over time, meaning static models often fail to account for the tail-end latency issues that occur during peak usage.
KV caching improves performance by storing the intermediate states of previous prompts, allowing the model to skip redundant computation. This is especially effective for repeated system prompts or long document prefixes, significantly reducing the time-to-first-token for subsequent requests.
Bottlenecking is primarily caused by head-of-line blocking, where long-running generative tasks stall shorter, faster requests. Mixing models with varying latency profiles on the same hardware without priority-based queuing leads to degraded performance for the faster tasks.
Measure success primarily through Time-to-First-Token (TTFT) and throughput variance. While total request duration is important, TTFT is the most accurate proxy for user-perceived performance in interactive applications, and throughput variance helps identify stability issues in your load-balancing logic.