A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
Microsoft Automatic Mixed Precision Library
Flux diffusion model implementation using quantized fp8 matmul & remaining layers use faster half precision accumulate, which is ~2x faster on consumer devices.
Cog Single GPU Quantized Implementation of Step-Video-T2V
A modular, accelerator-ready machine learning framework built in Go that speaks float8/16/32/64. Designed with clean architecture, strong typing, and native concurrency for scalable, production-ready AI systems. Ideal for engineers who value simplicity, speed, and maintainability.
FP8 dtypes enumeration in python
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