GeoFP8: Geometry-Aware FP8 Gradient Compression for Distributed LLM Training
Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision formats, such as FP8 and NVFP4, can significantly reduce the communication volume. Existing methods quantize gradients via linear or nonlinear mappings in Euclidean space, often degrading model performance because highly anisotropic gradients incur direction-dependent distortion. We present GeoFP8, a geometry-informed gradient scaling method that performs low-precision communication in geometry-aware coordinates. By transforming gradients into a near-isotropic space before quantization, GeoFP8 makes low-precision representations substantially more fait...
| Authors | Jieying Wang, Zizhong Wang, Fangru Linghu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 8 July 2026 |
| Last updated at source | 30 September 2026 |
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