arXiv:2607.01678v1 Announce Type: new Abstract: Communication increasingly dominates the cost of Large Language Model (LLM) pre-training, especially under data-parallel and sharded training schemes, where gradient synchronization and parameter reconstruction overhead increase with model size and system scale. Existing communication-reduction methods either sparsify raw gradients, which can be unstable for modern Adam-style optimizers at high sparsity, or quantize communication, whose savings are fundamentally bounded by bit width and often incur additional runtime overhead. We present SCAPE, a
The increasing scale of LLMs and the corresponding communication overhead during training make efficient communication methods a critical and immediate bottleneck.
This research addresses a fundamental efficiency challenge in training large AI models, directly impacting the economic viability and scalability of advanced AI development.
Optimized communication methods could significantly reduce the cost and time required for LLM training, allowing for larger, more capable models to be developed faster.
- · AI compute infrastructure providers
- · Hyperscalers
- · LLM developers
- · AI research institutions
- · Inefficient AI training hardware/software
Cheaper and faster LLM training leads to quicker iteration and deployment of new AI models.
Increased accessibility and competition in the AI model development space, potentially lowering barriers to entry.
Accelerated progress in AI capabilities across various sectors, driven by more efficient foundational model development.
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Read at arXiv cs.LG