arXiv:2511.10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution. Such modeling is essential for pre-deployment system-level optimizations (e.g., parallelization strategies) and hardware design-space explorations. While recent efforts have proposed collecting execution traces from real systems, access to large-scale infrastructure remains limited to major cloud providers. Moreover, traces capturing execution on
The increasing complexity and scale of LLMs demand more efficient resource utilization, making scalable synthesis of distributed workloads a critical and timely research area.
This research provides a mechanism to optimize distributed LLM performance, directly impacting the cost and efficiency of AI development and deployment for major players.
The ability to model and optimize distributed LLM workloads prior to deployment will accelerate development cycles and potentially reduce computational waste.
- · Cloud providers
- · Large language model developers
- · Hardware manufacturers
- · Researchers in distributed AI
- · Inefficient AI compute architectures
- · Companies without access to advanced optimization techniques
Improved performance and reduced cost for training and inference of large language models.
Accelerated development of more complex and capable AI models due to optimized infrastructure.
Enhanced competition in the AI sector as the barriers to efficient large-scale AI operations are lowered for sophisticated actors.
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Read at arXiv cs.AI