Company Performance Metrics
Prashanth V.: Co-founder.- Jason Qu: Chief Engineer
InferX is a serverless inference platform designed to run large numbers of machine learning models efficiently under real-world traffic. The platform focuses on eliminating cold starts, enabling fast model switching, and maintaining high GPU utilization in environments where many models are served with irregular demand.
Unlike “serverless GPU”
offerings that abstract infrastructure provisioning, InferX operates at the runtime layer, treating models as swappable execution state rather than long-lived GPU allocations. This allows inference workloads to scale on demand without pinning GPUs to individual models or paying the latency and cost penalties of repeated model loading.
InferX is built for long-tail inference scenarios, including multi-model APIs, agentic systems, and platforms serving large model catalogs. The platform is hardware-agnostic and can run on existing GPU infrastructure, cloud environments, or specialized accelerators, integrating underneath existing inference stacks rather than replacing them.
The company focuses on inference efficiency as a systems problem, with the goal of making large-scale, multi-model inference practical, predictable, and cost-efficient.
