Velda, a company based in San Francisco, has introduced a serverless GPU platform designed to simplify the process of running compute-intensive tasks for AI developers and batch workloads. The platform allows developers to launch training jobs, batch inference pipelines, and distributed workloads directly from their local development environment with a single command prefix.

Streamlining AI Development

The core of the Velda experience is the vrun command, which enables developers to instantly offload compute-heavy workloads to powerful cloud GPUs without modifying a single line of code. This approach addresses one of the most persistent frustrations in modern AI development: the gap between writing code and running it at scale.

Traditional workflows require developers to package their environment into a container, push it to a registry, define cluster configurations, and then wait for provisioning before a single training epoch begins. With Velda, that entire overhead disappears. The platform syncs the local environment directly to the cloud, ensuring that what runs on a developer’s laptop runs identically on a distributed GPU cluster — without the risk of dependency drift or environment mismatch.

Support for Various Workloads

Beyond individual training runs, Velda also supports robotics and physical AI workloads, making it relevant not only for language model training and batch inference but also for simulation-heavy research and embodied AI development. Teams working on robotic systems, reinforcement learning environments, and physical AI pipelines can leverage the same frictionless compute access that makes Velda compelling for traditional deep learning workflows.

The platform is available in two tiers: Velda Cloud, a managed offering designed for individual developers and small teams, and an enterprise tier with self-hosted or dedicated deployment options and premium support.

Original reporting: KTBS 3 (Shreveport) — read the source article.