ZeroGPU

ZeroGPU positions itself as the compute-efficient layer for AI inference, emphasizing smart routing of requests to smaller, more cost-appropriate models rather than defaulting every inference call to the largest and most expensive model available regardless of task complexity. This approach is designed to deliver faster response times and meaningfully lower per-request costs for production AI workloads where many incoming requests don't actually require a frontier-scale model to produce a correct or sufficiently high-quality answer. By intelligently matching each request to the smallest model capable of handling it well, ZeroGPU aims to help engineering teams running AI features at scale avoid the common trap of over-provisioning expensive model capacity for tasks that simpler, cheaper models could handle just as effectively. The platform fits into the broader 2026 trend of teams moving from a single default model toward multi-model orchestration strategies that route different tasks based on cost, latency, and required capability.