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In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We begin by setting up the full environment, installing the required libraries, loading a compact Instruct model, and preparing a simple workflow that runs in Colab while still demonstrating the real value of KV cache compression. As we move through implementation, we create a synthetic long-context corpus, define targeted extraction questions, and run […]

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Modern AI is no longer powered by a single type of processor—it runs on a diverse ecosystem of specialized compute architectures, each making deliberate tradeoffs between flexibility, parallelism, and memory efficiency. While traditional systems relied heavily on CPUs, today’s AI workloads are distributed across GPUs for massive parallel computation, NPUs for efficient on-device inference, and TPUs designed specifically for neural network execution with optimized data flow.  Emerging innovations like Groq’s LPU further push the boundaries, delivering significantly faster […]

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Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces correct outputs has historically meant substantial custom engineering work. NVIDIA AI team is now open-sourcing a toolkit designed to collapse that effort into […]

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In this tutorial, we build and run a complete Pose2Sim pipeline on Colab to understand how markerless 3D kinematics works in practice. We begin with environment setup, configure the project for Colab’s headless runtime, and then walk through calibration, 2D pose estimation, synchronization, person association, triangulation, filtering, marker augmentation, and OpenSim-based kinematics. As we progress, we not only execute each stage of the workflow but also inspect the generated outputs, visualize trajectories and joint angles, and learn how […]

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Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step reasoning. Researchers at Tongyi Lab, Alibaba Group introduced ‘VimRAG’, a framework built specifically to address that breakdown. The problem: linear history and compressed […]

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