Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data transfers. FP8 precision solves this on newer hardware, but what about the millions of RTX 30 and 20 series GPUs already deployed? Feather demonstrates that software-based FP8 emulation through bitwise packing can achieve near-theoretical 4x bandwidth improvements (3.3x measured), making efficient deep learning accessible without expensive hardware upgrades
The post Breaking the Hardware Barrier: Software FP8 for Older GPUs appeared first on Towards Data Science. Read More
