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Explore PyTorch data pipeline optimization techniques and cross-architecture kernel development for NVIDIA CUDA and AMD ROCm GPUs, with practical insights on solving bottlenecks and writing efficient cross-hardware code.
Explore LLM inference engine optimizations comparing vLLM, SGLang, and TensorRT for high-performance AI agent deployments with expert insights from Modal and AWS practitioners.
Master advanced techniques for pre-training and fine-tuning Mixtral MoE models using AWS SageMaker HyperPod and SLURM, covering implementation strategies and continued pre-training approaches.
Master advanced LLM fine-tuning techniques using Amazon SageMaker, exploring PEFT, RLHF, and PPO methodologies for optimized AI model performance and customization.
Explore the latest advancements in generative AI, from CodeWhisperer to ChatGPT, while learning about prompt engineering and reinforcement learning techniques for improved human-AI interaction.
Explore NVIDIA Dynamo's disaggregated LLM serving architecture and Luminal's PyTorch/CUDA kernel optimization techniques for enhanced AI performance engineering.
Discover how to achieve lightning-fast AI inference across NVIDIA and AMD GPUs using Modular's platform, covering MAX framework, Mojo language, and cost optimization strategies.
Master high-performance data curation and visualization for LLMs using NVIDIA CuML, DBSCAN, and tSNE algorithms with GPU acceleration techniques.
Discover key AI performance insights and hot topics from Neurips 2025 with expert analysis on cutting-edge systems optimization and engineering breakthroughs.
Discover how to automate browser interactions using Amazon's AGI technology in this focused 15-minute session with expert insights and practical implementation strategies.
Dive into NVIDIA Nsight Systems GPU profiling tools to optimize PyTorch LLM and computer vision workloads, focusing on data transfer optimization and performance engineering techniques.
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