NVIDIA (NIM)

NVIDIA NIM provides GPU-accelerated inference via OpenAI-compatible API at build.nvidia.com. Hosts Nemotron models and open-source LLMs with free API credits for experimentation. NGC API key auth.

NVIDIA NIM (NVIDIA Inference Microservices) is NVIDIA's platform for GPU-accelerated model inference. Available both as a hosted API at build.nvidia.com and as self-hostable Docker containers for on-premises deployment, NIM provides optimized inference for NVIDIA's own Nemotron models as well as popular open-source models like Llama. The flagship offering is Nemotron 3 Super — a 120B-parameter hybrid Mamba-2/Transformer MoE model announced at GTC in March 2026. With only ~12.7B parameters active per token, it delivers 449 output tokens/sec (ranked #1 in its tier by Artificial Analysis) while maintaining a 1M-token context window with 91.75% accuracy on RULER benchmarks. It's specifically optimized for agentic workloads: multi-step reasoning, tool use, and long-context tasks. NVIDIA's hosted API at build.nvidia.com provides free credits for experimentation with every NGC account. The API is fully OpenAI-compatible, making integration with OpenClaw straightforward. Models are served through NVIDIA's optimized inference stack including TensorRT-LLM and vLLM backends, delivering consistently low latency on NVIDIA GPU infrastructure. For OpenClaw users, NVIDIA NIM is compelling as a provider for open-source models with enterprise-grade inference performance. The Nemotron 3 Super model in particular offers strong agentic capabilities at a fraction of frontier model pricing — roughly 25x cheaper than GPT-5.4 on input tokens. Self-hosting via NIM containers is also an option for users with NVIDIA GPU infrastructure who want full control. The platform also hosts embedding models, making it useful beyond just chat completions. NIM containers support tensor parallelism, pipeline parallelism, and advanced quantization (FP8, INT4) for efficient deployment on various NVIDIA GPU configurations from RTX workstations to multi-GPU data center setups.

Tags: nvidia, nemotron, ngc, openai-compatible, gpu-accelerated, nim, open-source, agentic

Use Cases

  • Cost-effective agentic AI with strong tool use and reasoning capabilities
  • Long-context document processing (1M tokens) with high accuracy
  • SWE-Bench coding tasks — 60.47% verified, strong for open-source
  • Self-hosted GPU inference with NIM containers for full data control
  • OpenClaw cron jobs and background tasks where frontier quality isn't required
  • Embedding generation alongside chat completions from a single provider

Tips

  • Start with free credits for testing. Nemotron 3 Super is the best value model on the platform for agentic workloads.
  • For cost-sensitive OpenClaw setups, Nemotron 3 Super at $0.30/$0.80 per MTok is 25x cheaper than GPT-5.4.
  • The 1M context window on Nemotron 3 Super is genuinely usable — 91.75% accuracy on RULER at full length.
  • Use Nemotron 3 Nano (30B) for lightweight tasks. Active params are only 3B, making it very fast and cheap.
  • NIM containers can be self-hosted on your own NVIDIA GPUs for zero per-token cost after hardware investment.
  • Check provider availability on OpenRouter — Nemotron models are also available through DeepInfra, Fireworks, and Together at varying prices.

Known Issues & Gotchas

  • Free API credits are limited and can run out quickly with heavy usage. Monitor your balance at build.nvidia.com.
  • Self-hosting Nemotron 3 Super requires 8x H100-80GB GPUs at BF16 precision — serious hardware investment.
  • Not all models on build.nvidia.com are available for API use vs. just playground testing. Check API availability per model.
  • NGC API keys have the nvapi- prefix. Don't confuse with other NVIDIA credential types.
  • Nemotron 3 Super excels at agentic/coding tasks but lags behind frontier models on conversational quality (Arena-Hard V2: 73.88% vs GPT-OSS 90.26%).
  • Credit allocation and pricing can change — NVIDIA has adjusted the credit system multiple times.
  • Vision is not supported on Nemotron text models. Use separate vision models if needed.

Alternatives

  • Together AI
  • DeepInfra
  • Fireworks AI
  • Ollama (self-hosted)

Community Feedback

Nemotron 3 Super is insanely fast at 449 tok/s. The hybrid Mamba architecture really pays off for long-context agentic workloads. SWE-Bench at 60% for an open model is impressive.

— Reddit r/LocalLLaMA

NIM API credits are generous for experimentation but the credit system can be confusing. Some users report running out quickly with heavy testing.

— Hacker News

The NIM containers are great for self-hosting but require significant GPU resources. 8x H100-80GB for Nemotron 3 Super at BF16 is not cheap.

— NVIDIA Developer Forums

Configuration Examples

Basic NVIDIA NIM setup with Nemotron 3 Super

providers:
  nvidia:
    apiKey: nvapi-xxxxxxxxxxxxxxxxx
    model: nvidia/nvidia/nemotron-3-super-120b-a12b

NVIDIA as cost-effective fallback

providers:
  anthropic:
    apiKey: sk-ant-xxxxx
    model: anthropic/claude-sonnet-4-6
  nvidia:
    apiKey: nvapi-xxxxxxxxxxxxxxxxx
    model: nvidia/nvidia/nemotron-3-super-120b-a12b
    # 25x cheaper than GPT-5.4 for agentic tasks

Nemotron Nano for lightweight tasks

providers:
  nvidia:
    apiKey: nvapi-xxxxxxxxxxxxxxxxx
    model: nvidia/nvidia/nemotron-3-nano-30b-a3b
    # Ultra-fast, ultra-cheap for simple tasks