Twelve practical tips for designing AI-driven HPC workflows
A preprint from arXiv offers twelve practical guidelines for researchers designing AI and foundation-model-driven workflows on HPC clusters. The guide addresses system-level challenges including containerisation, job arrays, feedback loop mechanics, and I/O optimisation for small files. The work targets the transition from deterministic linear pipelines to adaptive, probabilistic computational environments, with particular emphasis on computational biology use cases.
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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.
Scaling AI-based Data Processing with Hugging Face + Dask
Hugging Face published a blog post describing how to scale AI-based data processing pipelines by combining Hugging Face datasets and models with Dask, a parallel computing framework. The post covers patterns for distributed inference and large-scale dataset preprocessing. This is a practical integration guide targeting ML engineers who need to process data at scale beyond single-machine limits.
Framework for Evaluating Datacenter Power Delivery Hierarchies for AI Workloads
Researchers from Microsoft Azure present a simulation framework for evaluating datacenter power delivery designs under AI-era conditions, where rack power density is projected to approach 1MW per deployment by 2027. The framework combines GPU/compute/storage projection models with production operational data to assess throughput, power, and cost metrics across realistic deployment sequences. Key findings show that multi-resource stranding materially affects deployable capacity and effective capital expenditure, and that the correct planning objective is deployable capacity over time rather than installed megawatts. The work addresses the challenge of designing power hierarchies that remain efficient across multiple hardware generations as AI accelerator density rises.
Accelerate ND-Parallel: A Guide to Efficient Multi-GPU Training
Hugging Face published a guide on N-dimensional parallelism for multi-GPU training using the Accelerate library. The post covers combining data parallelism, tensor parallelism, pipeline parallelism, and other strategies to efficiently scale model training across GPU clusters. This is a practical technical resource aimed at practitioners working with large-scale distributed training setups.
Fine-tuning Stable Diffusion models on Intel CPUs
This Hugging Face blog post describes a workflow for fine-tuning Stable Diffusion image generation models on Intel CPUs rather than GPUs. It covers the tooling and optimizations required to make CPU-based diffusion model training practical, relevant to inference-economics and hardware diversification trends. The post targets practitioners looking to reduce dependency on GPU hardware for generative model fine-tuning.
Making AI Work: Leadership, Lab, and Crowd
This commentary from One Useful Thing proposes a framework for organizational AI adoption centered on three elements: leadership commitment, structured experimentation (lab), and distributed employee engagement (crowd). The piece offers practical guidance for companies navigating AI integration. As a tier-2 commentary source, it reflects practitioner thinking on enterprise AI deployment patterns rather than reporting new technical developments.
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.
Techniques for Training Large Neural Networks
OpenAI published a technical overview of the engineering and research challenges involved in training large neural networks across GPU clusters. The post covers the distributed computing and synchronization techniques required to orchestrate large-scale training runs. This serves as a reference document for the infrastructure and methods underpinning frontier model development.