Benchmarking FP8 Stability: Where Gradients Go to Die
FP8 is the new frontier for training efficiency, but it breaks in the most sensitive layers. We dissect the E4M3/E5M2 split and how to spot divergence.
Tag: NVIDIA
AI Quantization and Hardware Co-Design
Explore how quantization and hardware co-design overcome memory bottlenecks, comparing NVIDIA and Google architectures while looking toward the 1-bit future of efficient AI model development.
Blackwell's Sparse Attention Engines: The Reality of FP4
FP4 isn't just 'lower precision' - it requires a fundamental rethink of activation outliers. We dive into the bit-level implementation of NVFP4, Micro-Tensor Scaling, and the new Tensor Memory hierarchy.