Evaluating model speed solely by tokens per second hides critical execution realities under prolonged multi-turn interactions. Time to first token and inter-token latency behave distinctly as sequence lengths scale into tens of thousands of tokens. Real-world applications demand rigorous benchmarking that exposes KV cache allocation overhead and memory fragmentation.
The KV Cache Memory Bottleneck
As context windows expand, storing key-value tensors for every preceding token consumes vast quantities of high-bandwidth memory. Standard contiguous allocation strategies lead to severe memory fragmentation, forcing GPUs to idle while waiting for free memory blocks. Dynamic memory management mechanisms solve this allocation inefficiency, but they introduce subtle latency spikes during heavy concurrency.
Time to First Token Versus Prefill Efficiency
The prefill phase processes the entire prompt in parallel, making it compute-bound and highly sensitive to matrix multiplication dimensions. Conversely, the generation phase runs autoregressively, turning into a memory-bound operation for each subsequent token. Balancing these two phases in a shared cluster requires isolated queues to maintain steady user-facing responsiveness.
Designing Benchmarks That Reflect Real System Pressure
Stop relying on single-prompt synthetic throughput metrics to validate model serving infrastructure. Measure latency distribution under realistic prefix-sharing ratios and fluctuating context lengths. System stability under peak load is achieved by capping context limits gracefully rather than allowing unconstrained KV cache growth to cause silent thrashing.