NVIDIA Is Letting You Try Dozens of AI Models for Free

NVIDIA Build currently lists 77 free endpoints, and you do not need a GPU to start using them.

What it is

NVIDIA Build is a catalog at build.nvidia.com/models where you can try a wide range of AI models directly in your browser and then connect them to your applications via API. As of the time of writing, the catalog shows 77 endpoints marked as free, spanning chat, coding, image generation, video generation, speech processing, computer vision, and even scientific research.

The concept is simple: pick a model, try it on the page, sign in to get an API key, and use NVIDIA's sample code to wire it into your app. Many of the endpoints are OpenAI-compatible, meaning if your application already speaks the OpenAI API format, you often only need to change the base URL, model name, and API key to switch to an NVIDIA-hosted model.

Why it matters

• Try before you buy. You can experiment with multiple models across different providers and tasks before committing to a paid API or investing in self-hosting infrastructure.
• No GPU required to start. The models run on NVIDIA's cloud infrastructure, so you can prototype AI features without owning or renting GPU hardware.
• OpenAI-compatible endpoints. Many endpoints accept the same request format as OpenAI's API, which reduces integration friction for existing applications and workflows.
• Broad task coverage. The catalog is not limited to chatbots. It includes models for content generation, coding assistance, image creation, video generation, speech, and domain-specific research.

How it works

An endpoint is essentially a gateway between your application and a model running on NVIDIA's servers. Your app sends a request (a prompt, an image, an audio clip) to the endpoint URL, NVIDIA runs the model, and the response comes back. The workflow is: visit build.nvidia.com/models, browse the catalog, select a model, try it in the browser, click to get an API key, and then use the provided code snippet to call the endpoint from your application.

For Python users, the integration often looks like creating an OpenAI client with a different base URL:

client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key="$NVIDIA_API_KEY")

From there, you call the model as you would any OpenAI-compatible chat completion, specifying the NVIDIA model name. NVIDIA also offers NIM containers for some models, which means if you later need to run the same model on your own infrastructure for data control or performance reasons, there is a path from cloud trial to self-hosted deployment.

Caveats

These are free endpoints for experimentation and development, not unlimited production API access. They come with quotas and rate limits, and NVIDIA can change access terms at any time. If you are building something that will serve real users, you need a cost and infrastructure plan that does not depend on free-tier limits.