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AI/ML

Developer Pods: A Self-Service GPU Experience That Feels Instant

In Part 1, we discussed the core problem: most organizations still deliver GPU access through the wrong abstraction. Developers do not want tickets, YAML, and long wait times. They want a working environment with the right tools and GPU access, available when they need it.

In this post, let’s look at the other half of the story: the end-user experience. Specifically, what does self-service actually look like for a developer or data scientist using Rafay Developer Pods?

The answer is simple: a familiar UI, a few guided choices, and a running environment they can SSH into in about 30 seconds.

New Developer Pod

Instant Developer Pods: Rethinking GPU Access for AI Teams

It's the week of KubeCon Europe 2026 in Amsterdam. Much of the conversations will be about Kubernetes, AI and GPUs. Let's have a honest discussion.

We are in 2026 and we’re still handing out infrastructure like it’s 2008. The entire workflow is slow, expensive and wildly inefficient. Meanwhile, your most expensive resource—GPUs—sit idle or underutilized.

The way most enterprises deliver GPU access today is completely misaligned with how developers and data scientists actually work. A developer wants to:

  • Run a PyTorch experiment
  • Fine-tune a model
  • Test a pipeline

What do they get instead?

A ticketing system with a multi day wait time and then finally a bloated VM or an entire bare-metal GPU server

There has to be a better way. This is the first part of a blog series on Rafay's Developer Pods. In this, we will describe why and how many of our customers have completely transformed the way they deliver their end users with a self service experience to GPUs.

Dev Pod

Introduction to Jupyter Notebooks

Jupyter Notebook is open-source software created and maintained by the Jupyter community. A Jupyter notebook allows for the creation and sharing of documents with code and rich text elements. It works with over 40 programming languages including Python, R, and Ruby making it versatile and flexible for data scientists. In this introductory blog to Jupyter notebooks, we will look at "why it exists" and "what it looks like".

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