Supercharging LLM Applications on Windows PCs with NVIDIA RTX Systems

Supercharge Your Windows PC with NVIDIA RTX for Next-Gen LLM Applications

Summary

NVIDIA RTX systems are revolutionizing the way we interact with computers by enabling local large language model (LLM) applications on Windows PCs. This shift from cloud-based to local processing offers numerous benefits, including cost savings, always-on availability, improved performance, and enhanced data privacy. With NVIDIA’s end-to-end developer tools, creating and deploying LLM applications on NVIDIA RTX AI-ready PCs has never been easier.

The Rise of LLM Applications

Large language models are fundamentally changing human-computer interaction. They are being integrated into a wide range of applications, from internet search to office productivity tools, advancing real-time content generation, text summarization, customer service chatbots, and question-answering use cases.

Benefits of Running LLMs Locally

Running LLMs locally on PCs offers several advantages:

• Cost: No cloud-hosted API or infrastructure costs for LLM inference. Directly access your compute resources.
• Always-on: Availability of LLM capabilities everywhere you go, without relying on high-bandwidth network connectivity.
• Performance: Latency is independent of network quality, offering lower latency as the entire model is running locally. This can be important for real-time use cases such as gaming or video conferencing.
• Data Privacy: Private and proprietary data can always stay on the device.

NVIDIA RTX for LLM Applications

NVIDIA RTX systems provide the fastest PC accelerator with up to 1300 TOPS, making them ideal for running LLM applications locally. With over 100M systems shipped, NVIDIA RTX offers a large installed base of users for new LLM-powered applications.

Developer Tools for LLM Applications

NVIDIA provides several end-to-end developer tools for creating and deploying LLM applications on NVIDIA RTX AI-ready PCs:

• NVIDIA TensorRT-LLM: An open-source large language model inference library that provides an easy-to-use Python API to define LLMs and build TensorRT engines.
• Pre-optimized Models: Access pre-optimized models on HuggingFace, NGC, and NVIDIA AI Foundations.
• Model Quantization: Use the TensorRT-LLM Quantization Toolkit for model quantization, enabling models to occupy a smaller memory footprint.
• Native Connectors: Native connectors for TensorRT-LLM to popular application frameworks such as LlamaIndex, offering seamless integration on Windows PCs.

Reference Applications

NVIDIA has developed two open-source developer reference applications:

• RAG on Windows using TensorRT-LLM and LlamaIndex: A retrieval augmented generation project running entirely on Windows PC with an NVIDIA RTX GPU and using TensorRT-LLM and LlamaIndex.
• Continue.dev Visual Studio Code Extension on PC with CodeLlama-13B: A reference project that runs the popular continue.dev plugin entirely on a local Windows PC, with a web server for OpenAI Chat API compatibility.

Developer Workflows for LLMs on NVIDIA RTX

Developers can now seamlessly run LLMs on NVIDIA RTX AI-ready PCs with the following options:

• Access Pre-optimized Models: Access pre-optimized models on HuggingFace, NGC, and NVIDIA AI Foundations.
• Train or Customize Models: Train or customize models on custom data in NVIDIA DGX Cloud with NVIDIA NeMo Framework.
• Quantize and Optimize Models: Quantize and optimize models for best performance on NVIDIA RTX with TensorRT-LLM.

Getting Started

To get started with developing LLM-based applications and projects, visit the NVIDIA developer blog for more information on getting started with generative AI development on Windows PCs with NVIDIA RTX systems.

Table: Pre-optimized Text-Based LLMs

Table: Supported GPU Architectures for TensorRT-LLM

Table: Minimum System Requirements

Conclusion

NVIDIA RTX systems are empowering developers to create and deploy next-gen LLM applications on Windows PCs, offering a range of benefits and tools to make this process seamless. With the latest updates, developers can now use popular community models and frameworks in the same workflow to build applications that run either in the cloud or locally on Windows PCs with NVIDIA RTX. This opens up new possibilities for gaming, creativity, productivity, and developer experiences, leveraging the power of local processing to transform human-computer interaction.