Introducing the AMD 5th Gen EPYC™ CPU

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

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

AMD and OpenAI announce strategic partnership to deploy 6 gigawatts of AMD GPUs

AMD and OpenAI have announced a multi-year partnership to deploy 6 gigawatts of AMD Instinct GPUs, beginning with 1 gigawatt in 2026, to power OpenAI’s next-generation AI infrastructure and accelerate global AI innovation.

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

Creating custom kernels for the AMD MI300

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

TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs

arXiv:2606. 11357v1 Announce Type: cross Abstract: With the growing demand for on-device LLM inference, edge SoCs increasingly integrate NPUs to improve performance and energy efficiency under tight power and thermal budgets.

BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators

arXiv:2607. 19438v1 Announce Type: cross Abstract: Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed through the Metal~4 tensor API.

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.

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.

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM

arXiv:2607. 14622v1 Announce Type: cross Abstract: Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs.

CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG