NVIDIA has introduced native Python support for its CUDA platform, which allows developers to write CUDA code directly in Python without needing to rely on additional wrappers. This enhancement simplifies the process of leveraging GPU capabilities for machine learning and scientific computing, making it more accessible for Python users.

oLLM is a lightweight Python library designed for large-context LLM inference, allowing users to run substantial models on consumer-grade GPUs without quantization. The latest update includes support for various models, improved VRAM management, and additional features like AutoInference and multimodal capabilities, making it suitable for tasks involving large datasets and complex processing.

Python data science workflows can be significantly accelerated using GPU-compatible libraries like cuDF, cuML, and cuGraph with minimal code changes. The article highlights seven drop-in replacements for popular Python libraries, demonstrating how to leverage GPU acceleration to enhance performance on large datasets without altering existing code.

Kompute is a flexible GPU computing framework supported by the Linux Foundation, offering a Python module and C++ SDK for high-performance asynchronous and parallel processing. It enables easy integration with existing Vulkan applications and includes a robust codebase with extensive testing, making it suitable for machine learning, mobile development, and game development. The platform also supports community engagement through Discord and various educational resources like Colab Notebooks and conference talks.