NVIDIA NIM
Containerised inference microservices for running models on NVIDIA GPUs.
Why us on this
NIM packages popular models as optimised, ready-to-run containers behind a standard API. It is the quick route to a production-grade on-premise endpoint when a client already owns NVIDIA GPUs, and an alternative to assembling the serving stack ourselves with vLLM or Ollama.
More in AI models
The other tools we reach for on the same kind of work.
OpenAI GPTOpenAI
OpenAI's large language models, used through the API.
Runs open-weight language models on your own hardware.
GemmaGoogle
Google's open-weight language models, run on-premise.
High-throughput serving for open-weight models.
Anthropic's Claude models, used through the API and in our own engineering.
Google GeminiGoogle
Google's multimodal models, strong on long documents and images.
Alibaba's open-weight model family, from small to very large.
xAI's Grok models, available through an API.
Talk to us
Already running NVIDIA NIM?
Tell us what it is doing today and where it stops. We will say plainly whether we can move it, what that takes, and when it is the wrong tool for what you are trying to do.
