Hugging Face and AMD partner on accelerating state-of-the-art models for CPU and GPU platforms

Bringing serverless GPU inference to Hugging Face users

Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration

Hugging Face Models on Foundry Managed Compute

AMD + 🤗: Large Language Models Out-of-the-Box Acceleration with AMD GPU

Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs

Hugging Face on AMD Instinct MI300 GPU

Accelerating over 130,000 Hugging Face models with ONNX Runtime

Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

Accelerate a World of LLMs on Hugging Face with NVIDIA NIM

From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?

arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.