Quantization
Quantization is a technique that reduces model size and speeds up inference by converting high-precision weights to lower precision (e.g., 32-bit to 4-bit). It enables large models to run on consumer hardware with minimal accuracy loss.
Understanding Quantization
Quantization is a model compression technique that reduces the numerical precision of a neural network's parameters, typically converting 32-bit floating-point weights to lower-bit representations like 16-bit, 8-bit, or even 4-bit integers. This dramatically reduces model size, memory footprint, and inference latency, making it possible to deploy large language models on consumer hardware and mobile devices. Post-training quantization applies conversion after training is complete, while quantization-aware training incorporates precision reduction during the training process for better accuracy retention. Tools like GPTQ, GGML, and bitsandbytes have made quantization accessible for large language models, enabling users to run billion-parameter models on personal computers. The technique involves a tradeoff between compression ratio and model quality, though modern methods preserve most performance even at aggressive compression levels. Quantization works synergistically with pruning and knowledge distillation as complementary approaches to efficient AI deployment.
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AI Infrastructure
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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.