Good news if you want to build with AI and have no budget: NVIDIA’s build.nvidia.com lets you try leading models right in the browser, call them through a free API tier, and start from ready-made reference workflows called NIM Agent Blueprints. The model layer is now genuinely free to start with, and the rest of a real solution can be built on free tiers too. This is one of the easiest places to begin building today.

A dark floor lit by a grid of red neon seams stretching into the distance, the infrastructure layer every AI model runs on.
Model playgrounds let you try the inference layer before you build anything around it.

What launched

build.nvidia.com brings three things together in one place:

  • Model playgrounds. Open a model card and run it in the browser with your own prompts, no install and no GPU of your own. The catalog spans NVIDIA’s own models plus widely used open-weight families. Each playground shows the exact API call, so moving from “trying it” to “calling it from code” is a copy and paste.
  • Free inference endpoints. A free tier lets you call those models from your own app while you prototype, through an OpenAI-compatible API.
  • NIM Agent Blueprints. Reference workflows for common applications (RAG over documents, customer-service assistants, agentic systems, and more), per NVIDIA’s announcement . Each one bundles partner microservices, reference code, customization docs, and a Helm chart to deploy.

Underneath, the models and blueprints run on NVIDIA NIM, a set of GPU-accelerated inference microservices.

What you can actually do with it today

  • Compare models side by side. Try the same prompt across several models in their playgrounds before you commit to one. Pair this with the 2026 LLM landscape to read the trade-offs.
  • Prototype against the free API. Wire a model into a small app using the free endpoints, then swap providers later if you need to.
  • Start from a blueprint. Use a reference workflow as a working skeleton for RAG or an agent, then replace the pieces you want to own.

For trying models and starting a project on the inference layer, this is genuinely useful, and free to begin.

You can build the whole thing for free now

This is the exciting part: the free model layer on build.nvidia.com plus a few other free tiers means you can build and ship a real AI project today without paying for anything. NVIDIA gives you the models and a free API; here are the other free pieces and how to use them:

  • Free GPUs and notebooks: Google Colab, Kaggle Notebooks, and Hugging Face Spaces give you GPU time at no cost for learning and small projects.
  • Free model access beyond NVIDIA: open-weight models (Llama, Mistral, Qwen, Gemma) run on modest hardware, and providers like Groq, Together AI, and OpenRouter offer free or trial API tiers.
  • Free build and hosting: GitHub Codespaces and local Docker for the dev environment, and the free tiers on AWS, Google Cloud, and Azure for hosting.

Use them together: try and compare models on build.nvidia.com, then build the rest of your stack cheaply. The AI Learning Galaxy pairs every concept with a small thing you ship, and working with multiple environments shows how to keep a second stage nearly free. This wiki stays vendor-independent, so you can pick NVIDIA, a competitor, or free open tools for the right reason.

Further reading