San Francisco – June 11, 2026 – As artificial intelligence development becomes increasingly compute-intensive, the infrastructure layer beneath it is struggling to keep up. Spinning up GPU clusters, managing Docker containers, writing Kubernetes manifests, and dealing with dependency drift have become significant time sinks for developers who simply want to run their models. Velda is changing that equation with a serverless GPU platform that lets AI teams launch training jobs, batch inference pipelines, and distributed workloads directly from their local development environment — with nothing more than a single command prefix.
The core of the Velda experience is the vrun command. By prepending vrun to any existing script or command, developers can instantly offload compute-heavy workloads to powerful cloud GPUs, including H200 instances, without modifying a single line of code. There are no image builds to manage, no manifest files to write, and no infrastructure configuration required. The local development experience remains entirely intact, while the computational muscle scales on demand in the background.
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.
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 is a managed offering designed for individual developers and small teams, providing instant access to VS Code in the browser, GPU compute, and a free monthly credit to get started. For larger organizations requiring dedicated infrastructure, custom security configurations, or premium support, Velda offers an enterprise tier with self-hosted or dedicated deployment options and the ability to book a dedicated onboarding session with the team.
As the demand for GPU compute continues to outpace the availability of easy-to-use infrastructure tooling, Velda stands out by prioritizing the developer experience above all else. By eliminating the operational overhead that typically sits between a developer’s idea and its execution at scale, the platform enables AI teams to move faster, iterate more freely, and focus entirely on building rather than managing infrastructure.
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