---
title: NVIDIA NIM
sidebar_position: 58
description: Use NVIDIA NIM hosted inference APIs with promptfoo to evaluate Llama, Qwen, Nemotron, DeepSeek, and Mistral chat models through OpenAI-compatible endpoints.
---

# NVIDIA NIM

The NVIDIA provider connects promptfoo to [NVIDIA's hosted inference API](https://build.nvidia.com) at `https://integrate.api.nvidia.com/v1`. The endpoint is OpenAI-compatible, so any model NVIDIA exposes through it can be used the same way you'd use OpenAI Chat Completions.

## Setup

Set your API key as an environment variable:

```bash
export NVIDIA_API_KEY=your_api_key_here
```

Or add it to your `.env` file:

```env
NVIDIA_API_KEY=your_api_key_here
```

### Getting an API key

1. Sign in at [build.nvidia.com](https://build.nvidia.com) (free developer account).
2. Open any model card (for example, [Llama 3.3 70B Instruct](https://build.nvidia.com/meta/llama-3_3-70b-instruct)).
3. Click **Get API Key**. The key starts with `nvapi-`.

Check your account at [build.nvidia.com](https://build.nvidia.com) for available credits and usage limits before running an eval.

## Configuration

Use the `nvidia:` prefix followed by the full model id as listed on the model card:

```yaml
providers:
  - nvidia:meta/llama-3.3-70b-instruct
  - nvidia:qwen/qwen2.5-coder-32b-instruct
  - nvidia:nvidia/nemotron-3-super-120b-a12b
```

Use `nvidia:<model>` without a subtype. `nvidia:chat:<model>`, `nvidia:embedding:<model>`, and other subtype forms are rejected.

Standard OpenAI-compatible parameters are passed through:

```yaml
providers:
  - id: nvidia:meta/llama-3.3-70b-instruct
    config:
      temperature: 0.7
      max_tokens: 1024
      top_p: 0.9
      stop: ['END']
```

To override the base URL (for example, when routing through a corporate proxy or to a self-hosted NIM):

```yaml
providers:
  - id: nvidia:meta/llama-3.3-70b-instruct
    config:
      apiBaseUrl: https://your-proxy.example.com/nvidia/v1
      apiKeyEnvar: CUSTOM_NVIDIA_KEY
```

`NVIDIA_API_BASE_URL` applies the same override to every NVIDIA provider without editing each
config. A `config.apiBaseUrl` takes precedence over it, and both take precedence over the default
`https://integrate.api.nvidia.com/v1`.

```bash
export NVIDIA_API_BASE_URL=https://your-proxy.example.com/nvidia/v1
```

## A few common models

The catalog changes over time. Copy the exact publisher/model ID from
[build.nvidia.com](https://build.nvidia.com/models) or NVIDIA's
[LLM API reference](https://docs.api.nvidia.com/nim/reference/llm-apis) before adding it to a
long-lived config.

## Example

A minimal eval comparing two NIM-hosted models. Uses deterministic assertions so the example runs end-to-end with only `NVIDIA_API_KEY` configured — `llm-rubric` would otherwise fall back to promptfoo's default OpenAI grader and require a separate `OPENAI_API_KEY`.

```yaml title="promptfooconfig.yaml"
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
providers:
  - id: nvidia:meta/llama-3.3-70b-instruct
    config:
      temperature: 0.2
      max_tokens: 256
  - id: nvidia:nvidia/nemotron-3-super-120b-a12b
    config:
      temperature: 1
      top_p: 0.95
      max_tokens: 1024
      passthrough:
        chat_template_kwargs:
          enable_thinking: false

prompts:
  - 'Summarise the following in one sentence: {{passage}}'

tests:
  - vars:
      passage: 'Photosynthesis is the process by which plants convert light energy into chemical energy stored in glucose.'
    assert:
      - type: icontains
        value: plants
      - type: icontains-any
        value: [light, energy, glucose]
```

The Nemotron configuration follows its [model-specific sampling guidance](https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b/modelcard) and disables reasoning for this short summarization task. Additional request fields such as `chat_template_kwargs` go under `config.passthrough`. If you enable reasoning, increase `max_tokens` to leave room for both reasoning and the final answer; the [hosted example](https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b) uses 16384.

These examples use a model in NVIDIA's [hosted chat catalog](https://docs.api.nvidia.com/nim/reference/llm-apis). Self-hosted NIM deployments can use their own served model identifiers.

If you want a model-graded assertion, point `llm-rubric` at a NIM-hosted grader so the example stays self-contained:

```yaml
defaultTest:
  options:
    provider: nvidia:meta/llama-3.3-70b-instruct
```

## Notes

- Cost calculation is not built in for NVIDIA models. NIM bills against credits rather than per-token public price lists for many models, and the actual cost depends on your account tier. Set both `inputCost` and `outputCost` on the provider config if you want to record an estimate in eval output.
- Tool calling and JSON-mode responses follow the same configuration as the [OpenAI provider](./openai.md) because the API surface is OpenAI-compatible. Streaming responses are not implemented by this provider.
- This provider supports NIM chat-completion models. Retrieval, embedding, reranking, and other NIM APIs require a provider that targets their corresponding endpoint.