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

An innovative library for efficient LLM inference via low-bit quantization

Flux diffusion model implementation using quantized fp8 matmul & remaining layers use faster half precision accumulate, which is ~2x faster on consumer devices.

JAX Scalify: end-to-end scaled arithmetics

Spike, a RISC-V ISA Simulator with added 8-bit vector floating point support

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.

Narrow precision floating point types

Python implementations for multi-precision quantization in computer vision and sensor fusion workloads, targeting the XR-NPE Mixed-Precision SIMD Neural Processing Engine. The code includes visual inertial odometry (VIO), object classification, and eye gaze extraction code in FP4, FP8, Posit4, Posit8, and BF16 formats.

Cog Single GPU Quantized Implementation of Step-Video-T2V

FP8 dtypes enumeration in python