Overshoot

AI teams deploying models with vLLM or SGLang get performance tuned for average workloads, not their specific hardware and model combination. The page gives concrete examples: FlashAttention has no kernel for Gemma 4's largest attention heads on the RTX PRO 6000, vLLM's Triton fallback underuses tensor cores on Gemma 4 attention, and state copies fill most of a request's memory on Qwen3.5 35B-A3B. These gaps mean teams are likely leaving significant throughput and latency on the table without custom kernel work or launch-setting changes.

Overshoot profiles a team's deployment against the theoretical hardware limit and closes the gap through custom kernel engineering and configuration changes. Examples from their blog include writing a custom attention kernel for Gemma 4 on the RTX PRO 6000 (1,770 ms to 464 ms), a fused MLP kernel combined with whole-step CUDA graphs (-9.3% per step), a caching change that doubled requests served per GPU, a rewritten multimodal processor (428 ms to 81 ms), and switching server-to-GPU communication to TCP (325 ms to 72 ms). Their pricing model is pay only if they meet the agreed performance target.