Demo: Optimizing Gemma inference on NVIDIA GPUs with TensorRT-LLM Share: Download MP3 Similar Tracks AI Inference: The Secret to AI's Superpowers IBM Technology The Evolution of Multi-GPU Inference in vLLM | Ray Summit 2024 Anyscale From model weights to API endpoint with TensorRT LLM: Philip Kiely and Pankaj Gupta AI Engineer Understanding the LLM Inference Workload - Mark Moyou, NVIDIA PyTorch RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models IBM Technology TensorRT for Beginners: A Tutorial on Deep Learning Inference Optimization Long's Short-Term Memory Model Context Protocol (MCP), clearly explained (why it matters) Greg Isenberg Long-Context LLM Extension Sasha Rush RAG vs. CAG: Solving Knowledge Gaps in AI Models IBM Technology Fast LLM Serving with vLLM and PagedAttention Anyscale Speculative Decoding: When Two LLMs are Faster than One Efficient NLP The Best RAG Technique Yet? Anthropic’s Contextual Retrieval Explained! Prompt Engineering 17 Python Libraries Every AI Engineer Should Know Dave Ebbelaar TensorRT-LLM: Quantization and Benchmarking Long's Short-Term Memory Quantization vs Pruning vs Distillation: Optimizing NNs for Inference Efficient NLP LoRA explained (and a bit about precision and quantization) DeepFindr Deep Dive: Optimizing LLM inference Julien Simon Understanding LLM Inference | NVIDIA Experts Deconstruct How AI Works DataCamp LoRA & QLoRA Fine-tuning Explained In-Depth Entry Point AI All You Need To Know About Running LLMs Locally bycloud
