NVIDIA's TensorRT-LLM: Building Powerful RAG Apps! (Opensource) 

In this video, we will be taking a looking at NVIDIA's TensorRT-LLM and how it streamlines the deployment and optimization of LLMs for diverse inference tasks, especially in desktop applications.
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[Link's Used]:
TensorRT-LLM Github Repo: github.com/NVI...
TensorRT Github Repo: github.com/pyt...
RAG App Example Demo: github.com/NVI...
Demo Video: / 1
Blog Post on Accelerating Inference with TensorRT: developer.nvid...
Blog Post on Supercharging LLM Applications on Windows PCs with NVIDIA RTX Systems: developer.nvid...
Guide on TensorRT: developer.nvid...
TensorRT-LLM Docs: nvidia.github....
Unravel the depths of TensorRT-LLM as we explore its user-friendly Python API, designed for seamlessly defining LLMs and constructing TensorRT engines. Learn how TensorRT-LLM incorporates state-of-the-art optimizations tailored for efficient inference on NVIDIA GPUs, enhancing performance and scalability. Discover the Python and C++ runtimes offered by TensorRT-LLM, enabling smooth execution of inference tasks with the generated TensorRT engines.
Dive into the significance of model quantization supported by TensorRT-LLM, a pivotal feature ensuring compatibility with PC GPUs while minimizing memory footprint. Explore the functionalities of the TensorRT-LLM Quantization Toolkit and its role in optimizing LLMs for enhanced performance. Gain valuable insights into how TensorRT-LLM empowers developers to navigate the complexities of LLM inference with ease, unlocking new possibilities in natural language processing and beyond.
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Additional Tags and Keywords:
NVIDIA TensorRT, Large Language Model, LLM Inference, TensorRT Engine, Python API, GPU Optimization, Model Quantization, Desktop Applications, Natural Language Processing
Hashtags:
#NVIDIATensorRT #largelanguagemodels #LLMInference #TensorRTEngine #GPUOptimization #ModelQuantization #DesktopApplications #naturallanguageprocessing, NVIDIA