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Overcoming Cross-Platform Deployment Hurdles in the Age of AI Processing Units

Unite.AI

Graphic Processing Units (GPUs): Originally designed for graphics rendering, GPUs have become essential for AI computations due to their parallel processing capabilities. While GPUs are optimized for throughput and can process large amounts of data in parallel, they may only be energy-efficient for some AI workloads.

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Setting Up a Training, Fine-Tuning, and Inferencing of LLMs with NVIDIA GPUs and CUDA

Unite.AI

One such library is cuDNN (CUDA Deep Neural Network library), which provides highly tuned implementations of standard routines used in deep neural networks. The model is first parsed and optimized by TensorRT, which generates a highly optimized inference engine tailored for the specific model and hardware.

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Quanda: A New Python Toolkit for Standardized Evaluation and Benchmarking of Training Data Attribution (TDA) in Explainable AI

Marktechpost

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Today, we talk about TDA, which aims to relate a model’s inference from a specific sample to its training data.

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No More Paid Endpoints: How to Create Your Own Free Text Generation Endpoints with Ease

Mlearning.ai

LLM from a CPU-Optimized (GGML) format: LLaMA.cpp is a C++ library that provides a high-performance inference engine for large language models (LLMs). It is based on the GGML (Graph Neural Network Machine Learning) library, which provides a fast and efficient way to represent and process graphs.

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The Story of Modular

Mlearning.ai

NNAPI   — The Android Neural Networks API (NNAPI) is an Android C API designed for running computationally intensive operations for machine learning on mobile devices and enables hardware-accelerated inference operations on Android devices. In order to tackle this, the team at Modular developed a modular inference engine.