**Whitepaper**

# GPU-Accelerated AI Inference

Get tips and best practices for deploying, running, and scaling AI models for inference for generative AI, large language models, recommender systems, computer vision, and more on NVIDIA’s AI inference platform.

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## What Will You Learn?

AI is driving breakthrough innovation across industries, but many projects fall short of expectations in production. Download this whitepaper to explore the evolving AI inference landscape, architectural considerations for optimal inference, end-to-end deep learning workflows, and how to take AI-enabled applications from prototype to production with the [NVIDIA’s AI inference platform](https://www.nvidia.com/en-us/data-center/resources/inference-technical-overview.md), including NVIDIA Triton™ Inference Server, NVIDIA TensorRT™, and NVIDIA TensorRT-LLM™.

## Challenges to GPU-Accelerated AI Inference

### Multiple Frameworks

Taking AI models into production can be challenging due to conflicts between model-building nuances and the operational realities of IT systems.

### Mixed Infrastructure

The ideal place to execute AI inference can vary, depending on the service or product that you’re integrating your AI models into.

### Scaling Deployment

Researchers are continuing to evolve and expand the size, complexity, and diversity of AI models.

### Disparate Inference Types

The NVIDIA AI inference platform delivers the performance, efficiency, and responsiveness that’s critical to powering the next generation of AI applications.

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