Inferless

Inferless enables users to deploy machine learning models on serverless GPUs within minutes, focusing on rapid and scalable model inference. The platform is designed to accommodate production workloads, providing infrastructure that can automatically scale from zero to hundreds of GPUs to handle spiky or unpredictable demand. Deployment is supported from multiple sources, including Hugging Face, Git, Docker, or directly from a command-line interface, and users can opt for automatic redeployment to streamline the model shipping process.

Key features of Inferless include the ability to customize container runtimes, allowing users to install the specific software and dependencies required for their models. The service provides NFS-like writable volumes that support simultaneous connections across multiple replicas, facilitating data access for distributed workloads. Automated CI/CD capabilities are available, enabling auto-rebuilds for models and removing the need for manual re-imports. For monitoring and optimization, Inferless offers detailed call and build logs to help users track and refine model performance during development.

Additional functionality includes dynamic batching, which combines server-side requests to increase throughput, and customizable private endpoints with options to adjust scaling, timeouts, and concurrency settings. The infrastructure is managed through an in-house load balancer that handles the automatic scaling of services with minimal overhead.

Inferless is intended for users deploying custom machine learning models who require scalable, production-ready GPU inference infrastructure.