NVIDIA NIM for Large Language Models

Large Language Models (RC15)

NVIDIA NIM for Large Language Models (LLMs) (NVIDIA NIM for LLMs) brings the power of state-of-the-art large language models (LLMs) to enterprise applications, providing unmatched natural language processing and understanding capabilities.

Whether developing chatbots, content analyzers, or any application that needs to understand and generate human language — NVIDIA NIM for LLMs is the fastest path to inference. Built on the NVIDIA software platform, NVIDIA NIM brings state of the art GPU accelerated large language model serving.

NVIDIA NIM for LLMs abstracts away model inference internals such as execution engine and runtime operations. NVIDIA NIM for LLMs provides the most performant option available whether it be with TensorRT, vLLM or LLM others.

Scalable Deployment: NVIDIA NIM for LLMs is performant and can easily and seamlessly scale from a few users to millions.

Advanced Language Models: Built on cutting-edge LLM architectures, NVIDIA NIM for LLMs provides optimized and pre-generated engines for a variety of popular models. NVIDIA NIM for LLMs includes tooling to help create GPU optimized models.

Flexible Integration: Easily incorporate the microservice into existing workflows and applications. NVIDIA NIM for LLMs provides an OpenAI API compatible programming model and custom NVIDIA extensions for additional functionality.

Enterprise-Grade Security: Data privacy is paramount. NVIDIA NIM for LLMs emphasizes security by using safetensors, constantly monitoring and patching CVEs in our stack and conducting internal penetration tests.

Chatbots & Virtual Assistants: Empower bots with human-like language understanding and responsiveness.

Content Generation & Summarization: Generate high-quality content or distill lengthy articles into concise summaries with ease.

Sentiment Analysis: Understand user sentiments in real-time, driving better business decisions.

Language Translation: Break language barriers with efficient and accurate translation services.

And many more… The potential applications of NVIDIA NIM for LLMs are vast, spanning across various industries and use-cases.

NVIDIA NIM for LLMs is one of what will become many NIMs. Each NIM is its own Docker container with a model, such as meta/llama3-8b-instruct. These containers include the runtime capable of running the model on any NVIDIA GPU. The NIM automatically downloads the model from NGC, leveraging a local filesystem cache if available. Each NIM is built from a common base, so once a NIM has been downloaded, downloading additional NIMs is extremely fast.

When a NIM is first deployed, NIM inspects the local hardware configuration, and the available model versions available in the model registry, and automatically chooses the best version of the model for the available hardware. For a subset of NVIDIA GPUs (see Support Matrix), NIM downloads the optimized TRT engine and runs an inference using the TRT-LLM library. For all other NVIDIA GPUs, NIM downloads a non-optimized model and runs it using the vLLM library.

NIM Lifecycle

flowchart TD A[User runs NIM<br/>`docker run ...`] --> B[Docker container downloads] B --> C{Is model on\nlocal filesystem?} C -->|No| D[Download model from NGC] C -->|Yes| E[Run the model] D --> E E --> F[Start OpenAI compliant<br/>Completions REST API server]

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