GPU
A GPU (Graphics Processing Unit) is a specialized processor designed for parallel computation that has become essential for training deep learning models. GPUs from NVIDIA dominate AI computing with architectures optimized for matrix operations.
Understanding GPU
A GPU (Graphics Processing Unit) is a specialized processor originally designed for rendering graphics but now essential for accelerating the parallel computations required by deep learning. Unlike CPUs that excel at sequential tasks, GPUs contain thousands of smaller cores optimized for simultaneous matrix operations, making them ideal for the tensor computations at the heart of neural network training and inference. NVIDIA GPUs paired with the CUDA platform dominate the AI hardware landscape, with models like the A100 and H100 powering distributed training of large foundation models. Cloud providers offer GPU instances that make this computing power accessible without upfront hardware investment. GPU memory capacity often determines the maximum model size and batch size during training, driving innovations in techniques like gradient accumulation and model parallelism. The demand for GPU compute has surged with the rise of generative AI, making GPUs a critical and sometimes scarce resource in the AI ecosystem.
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.