arXiv:2511.09557v4 Announce Type: replace-cross Abstract: As large language models (LLMs) continue to grow in size, distributed inference has become increasingly important. Model-parallel strategies must now efficiently scale not only across multiple GPUs but also across multiple nodes. In this work, we present a detailed performance study of multi-node distributed inference using LLMs on GPU-based supercomputers. We conduct experiments with several state-of-the-art inference engines alongside YALIS, a research-oriented prototype engine designed for controlled experimentation. We analyze the s
The continuous growth in LLM size necessitates more efficient distributed inference, making multi-node communication a critical current challenge in AI development.
Improving multi-node LLM inference directly impacts the cost and performance of large-scale AI deployment, which is crucial for sovereign AI ambitions and the widespread adoption of advanced AI systems.
The understanding and optimization of communication performance in multi-node LLM inference can lead to more accessible and powerful AI, fundamentally altering the infrastructure requirements and capabilities of LLMs.
- · Hyperscale cloud providers
- · GPU manufacturers
- · AI model developers
- · High-performance computing centers
- · Inefficient inference software developers
- · Organizations with limited compute access
- · Obsolete networking hardware manufacturers
More efficient and cost-effective deployment of ever-larger language models becomes possible.
Access to advanced large language model capabilities expands, potentially democratizing AI development and application.
Nations are better positioned to build sovereign AI capabilities, reducing dependency on a few dominant global players.
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Read at arXiv cs.LG