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This article explains how to dramatically speed up PyTorch model training using code optimizations, mixed‑precision, torch.compile, distributed data parallelism, and DeepSpeed, presenting benchmark results that show up to 11.5× acceleration on multiple GPUs while maintaining high accuracy.
The FlightVGM paper, awarded Best Paper at FPGA 2025, details a novel FPGA-based inference IP for video generation models that leverages time‑space activation sparsity, mixed‑precision DSP58 extensions, and adaptive scheduling to achieve up to 1.30× performance and 4.49× energy‑efficiency gains over a NVIDIA 3090 GPU while preserving model accuracy.
This article explains how GPU memory is allocated during model fine‑tuning, describes collective communication primitives, and compares data parallel, model parallel, ZeRO, pipeline parallel, mixed‑precision, and checkpointing techniques for reducing memory consumption in large‑scale AI training.
Tencent’s intelligent machine‑learning platform achieved a world‑record by training AlexNet in 4 minutes and ResNet‑50 in 6.6 minutes on ImageNet, using large batch sizes, mixed‑precision, LARS optimization, hierarchical synchronization, gradient fusion, and pipeline I/O techniques to overcome accuracy and scalability challenges.