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  What Are Industrial GPU Computers, and What Are They Used For? The rapid convergence of AI and automation technologies is driving the need for high-speed, large-scale data processing at the edge. Edge devices, which were once simple data collectors, now leverage AI models and machine learning algorithms to perform complex analysis directly on the data they gather. This shift demands significantly higher processing power, which traditional CPUs alone cannot provide. Industrial GPU computers bridge this gap by combining the strengths of CPUs and GPUs, delivering the performance needed for tasks like real-time image processing, data analysis, and machine learning inference. Originally deployed in data centres, these systems are increasingly being adopted at the edge, making them a cornerstone of the Edge AI era. This article explores the advantages, core features, and applications of industrial GPU computers, along with real-world use cases to illustrate their transformative potenti...

  intel OpenVino framework OpenVINO can help accelerate the processing of your local LLM (Large Language Model) application generation in several ways. OpenVINO can significantly aid in developing LLM and Generative AI applications on a local system like a laptop by providing optimized performance and efficient resource usage. Here are some key benefits: 1. Optimized Performance : OpenVINO optimizes models for Intel hardware, improving inference speed and efficiency, which is crucial for running complex LLM and Generative AI models on a laptop. 2. Hardware Acceleration : It leverages CPU, GPU, and other accelerators available on Intel platforms, making the most out of your laptop's hardware capabilities. 3. Ease of Integration : OpenVINO supports popular deep learning frameworks like TensorFlow, PyTorch, and ONNX, allowing seamless integration and conversion of pre-trained models into the OpenVINO format. 4. Edge Deployment : It is designed for edge deployment, making it suitable ...

  Photo by SevenStorm JUHASZIMRUS by pexel Differences Between CPU-based Multi-threading and Multi-processing CPU-based Multi-threading : - Concept: Uses multiple threads within a single process. - Shared Memory: Threads share the same memory space. - I/O Bound Tasks: Effective for tasks that spend a lot of time waiting for I/O operations. - Global Interpreter Lock (GIL): In Python, the GIL can be a limiting factor for CPU-bound tasks since it allows only one thread to execute Python bytecode at a time. CPU-based Multi-processing : - Concept: Uses multiple processes, each with its own memory space. - Separate Memory: Processes do not share memory, leading to more isolation. - CPU Bound Tasks: Effective for tasks that require significant CPU computation since each process can run on a different CPU core. - No GIL: Each process has its own Python interpreter and memory space, so the GIL is not an issue. CUDA with PyTorch : - Concept: Utilizes the GPU for parallel computation. - Massi...

  Chatbot Application with Local LLM, RAG, LangChain, and Guardrail I've developed a chatbot application designed for informative and engaging conversationAs you already aware that Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds, RAG can significantly improve the value of generative AI systems. Developers must consider a variety of factors when building a RAG pipeline: from LLM response benchmarking to selecting the right chunk size. In tapplication demopost, I demonstrate how to build a RAG pipeline uslocal LLM which can be converted to ing NVIDIA AI Endpoints for LangChain. FirI have you crdeate a vector storeconnecting with one of the ...

photo by pixabay Here's a breakdown of the key differences between Kubeflow and Airflow , specifically in the context of machine learning pipelines, with a focus on Large Language Models (LLMs): Kubeflow vs. Airflow for ML Pipelines (LLMs): Core Focus: Kubeflow: Kubeflow is a dedicated platform for machine learning workflows. It provides a comprehensive toolkit for building, deploying, and managing end-to-end ML pipelines, including functionalities for experiment tracking, model training, and deployment. Airflow: Airflow is a general-purpose workflow orchestration platform. While not specifically designed for ML, it can be used to automate various tasks within an ML pipeline. Strengths for LLMs: Kubeflow: ML-centric features: Kubeflow offers built-in features specifically beneficial for LLMs, such as Kubeflow Pipelines for defining and managing complex training workflows, Kubeflow Notebook for interactive development, and KFServing for deploying trained models. Sca...