Quantization Deep Dive: From FP32 to INT4
Master neural network quantization with interactive visualizations. Explore QAT, PTQ, GPTQ, AWQ, and SmoothQuant methods for efficient model deployment.
Technical Articles - Page 4
Deep dive into machine learning, computer vision, and software engineering. Expert insights on AI, local LLMs, quantization, and practical implementation details from real-world projects.
Articles - Page 4
How TensorRT Works: NVIDIA Inference Optimization
Explore TensorRT optimization: layer fusion, INT8 quantization, kernel auto-tuning, and deployment strategies with 8+ interactive visualizations.
Kernel Fusion in Deep Learning: How GPU Kernels Are Merged
Kernel fusion merges multiple neural network operations into a single GPU kernel to eliminate intermediate memory writes — this article explains how fusion works, why it helps deep learning workloads, and how TensorRT and torch.compile use it.
Python's logging Module Is Already a Singleton
You don't need a logging framework, registry, or factory. Python's stdlib logging is a global singleton — getLogger(name) always returns the same instance, and dictConfig can configure any logger in the process, including third-party libraries.
CPython Internals: How Python Really Works Under the Hood
Deep dive into CPython internals: bytecode compilation, memory management, the GIL, object model, and garbage collection.
C++ Compilation Process: From Source Code to Object Files
How C++ compilers transform source code through preprocessing, parsing, optimization, and code generation. Interactive visualizations included.
Showing 19-24 of 30 articles