Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel

The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator

Introducing Optimum: The Optimization Toolkit for Transformers at Scale

Accelerating PyTorch Transformers with Intel Sapphire Rapids - part 2

Accelerating PyTorch Transformers with Intel Sapphire Rapids - part 1

Native-speed vLLM transformers modeling backend

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.

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

arXiv:2608. 19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets.

Accelerating Hugging Face Transformers with AWS Inferentia2

Mistral AI partners with NVIDIA to accelerate open frontier models

MegaFold: Efficient Training of Next-Generation 3D Attention Protein Models on Cross-Platform GPUs

arXiv:2506. 20686v2 Announce Type: replace-cross Abstract: Recent advances in biomolecular modeling have been catalyzed by models such as AlphaFold3 (AF3), which introduce science-informed changes to the transformer architecture.

Accelerate your models with 🤗 Optimum Intel and OpenVINO

AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers

arXiv:2602. 13357v3 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising structure.