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

DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space

DC-Gen is a post‑training framework that accelerates text‑to‑image diffusion models by using a deeply compressed latent space. It first aligns the base model’s latent representations with a lightweight embedding alignment, then applies minimal LoRA fine‑tuning to preserve generation quality. Experiments on SANA and FLUX.1‑Krea show that DC‑Gen‑FLUX cuts 4K image generation latency by 53× on an NVIDIA H100 and, with NVFP4 SVDQuant, achieves a 138× total speedup on a single NVIDIA 5090 GPU.

Native-speed vLLM transformers modeling backend