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NVIDIA/TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 65%Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel →
- PossiblePossibly related (embedding) · 57%FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers →
- PossiblePossibly related (embedding) · 50%From Materials Simulation to Experimental Astronomy, New NVIDIA AI Software Unlocks Scientific Discoveries →
- PossiblePossibly related (embedding) · 50%GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study →
- PossiblePossibly related (embedding) · 47%Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs →
- PossiblePossibly related (embedding) · 46%Nvidia is sending GPUs to the Moon →
Covers
Implements
paperFlexViT: A Flexible FPGA-based Accelerator for Edge Vision TransformerspaperGPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative StudypaperEfficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs
Covers (incoming)
Related across the graph
paperEfficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUsnewsFrom Materials Simulation to Experimental Astronomy, New NVIDIA AI Software Unlocks Scientific DiscoveriesnewsNvidia is sending GPUs to the MoonpaperFlexViT: A Flexible FPGA-based Accelerator for Edge Vision TransformersnewsAccelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModelpaperGPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study