arXiv:2607.07494v1 Announce Type: cross Abstract: 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 GIFT, a geometry-informed gradient scaling method that performs low-precision communication in geometry-aware coordinat
The rapid scaling of Large Language Models (LLMs) is exposing gradient communication as a major bottleneck, driving innovation in low-precision methods to improve efficiency.
Improving gradient communication efficiency for LLMs directly addresses the core computational and energy costs associated with advanced AI training, critical for future scaling and deployment.
Current methods for low-precision gradient communication that degrade performance due to anisotropic gradients will be superseded by geometry-informed approaches that maintain model accuracy.
- · LLM developers
- · Hyperscalers
- · AI hardware manufacturers
- · Energy-constrained data centers
- · Inefficient AI training methodologies
- · Developers reliant on high-precision gradient communication
Reduced training times and infrastructure costs for large-scale AI models.
Accelerated development and deployment of more complex and larger LLMs due to improved training efficiency.
Potentially democratized access to training cutting-edge LLMs as the compute barrier is lowered.
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Read at arXiv cs.LG