Custom CUDA Kernels for All from Codex and Claude
A Hugging Face blog post describes using AI coding agents (Codex and Claude) to automatically generate custom CUDA kernels, lowering the barrier to GPU kernel development. The piece demonstrates agent-assisted GPU programming as a practical workflow for ML practitioners. This represents a concrete application of AI coding tools to the specialized domain of CUDA/GPU optimization.
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Related events (8)
We Got Claude to Build CUDA Kernels and Teach Open Models
A Hugging Face blog post describes using Claude to generate CUDA kernels and then distilling that knowledge into open-weight models. The approach combines LLM-assisted low-level GPU programming with knowledge transfer to smaller open models. This sits at the intersection of AI-assisted systems programming and open-weights capability improvement.
From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels
Hugging Face published a guide on building and scaling production-ready CUDA kernels, covering the full workflow from development to deployment. The post targets ML engineers who need to write custom GPU kernels for inference optimization and production workloads. It addresses practical concerns around kernel compilation, testing, and integration with existing ML frameworks.
Creating Custom Kernels for the AMD MI300
A Hugging Face blog post details the process of writing custom GPU kernels targeting the AMD MI300 accelerator. The post covers practical techniques for optimizing AI workloads on AMD hardware, contributing to the growing ecosystem of non-NVIDIA GPU support for ML inference and training. This is relevant to the broader trend of diversifying AI infrastructure beyond CUDA-dominant workflows.
How NVIDIA Engineers and Researchers Build with Codex
OpenAI published a case study describing how NVIDIA teams use Codex powered by GPT-5.5 to ship production systems and accelerate research experimentation. The piece highlights enterprise adoption of Codex as a coding agent in a major hardware/AI lab context. It signals continued real-world deployment of OpenAI's agentic coding tools at scale.
Hugging Face Launches Kernel Hub for Custom GPU Kernels
Hugging Face has introduced the Kernel Hub, a centralized repository for sharing and discovering custom GPU kernels optimized for AI/ML workloads. The platform aims to make high-performance custom CUDA and Triton kernels more accessible to the broader ML community. This represents an infrastructure layer addition to the Hugging Face ecosystem, complementing its existing model and dataset hubs.
Easily Build and Share ROCm Kernels with Hugging Face
Hugging Face has published a guide and tooling for building and sharing custom ROCm kernels on its platform, targeting AMD GPU users in the ML ecosystem. The post covers the workflow for packaging, uploading, and reusing ROCm-based GPGPU kernels via the Hub. This lowers the barrier for AMD GPU kernel development and sharing, complementing the existing CUDA-centric kernel ecosystem. The initiative is relevant to inference optimization and the broader push to diversify GPU hardware support in AI workloads.
OpenAI Releases Block-Sparse GPU Kernels for Sparse Neural Networks
OpenAI released optimized GPU kernels targeting block-sparse neural network architectures, claiming orders-of-magnitude speedups over cuBLAS and cuSPARSE depending on sparsity level. The kernels were applied to achieve state-of-the-art results in text sentiment analysis and generative modeling of text and images. This release represents an early infrastructure contribution toward efficient sparse computation in deep learning.
AINews: Agents for Everything Else — Codex for Knowledge Work, Claude for Creative Work
A Latent Space daily AI news digest reflecting on the expanding scope of coding agents beyond software development into knowledge work and creative work domains. The piece uses OpenAI Codex and Anthropic Claude as anchoring examples of agents 'breaking containment' from their original coding/assistant niches. Published as a quieter news day commentary, it surveys the broadening agent ecosystem landscape.