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STAGE framework synthesizes LLM execution graphs for distributed workloads · 2 sources tracked

By PulseAugur Editorial · [3 sources] ·

A new framework called STAGE has been developed to synthesize high-fidelity execution graphs for large language models (LLMs) and Mixture-of-Experts (MoEs). This framework aims to optimize distributed AI workloads by modeling various parallelization strategies, enabling exploration of different model architectures and system configurations without requiring direct access to large-scale infrastructure. STAGE has demonstrated its scalability by generating traces for over 128,000 GPUs, preserving tensor-level accuracy in compute, memory, and communication. AI

IMPACT Enables scalable exploration of distributed LLM training and inference configurations without requiring direct access to large-scale AI infrastructure.

RANK_REASON The cluster describes a new framework for synthesizing LLM execution graphs, detailed in an arXiv paper.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

STAGE framework synthesizes LLM execution graphs for distributed workloads · 2 sources tracked

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The cluster describes a new framework for synthesizing LLM execution graphs, detailed in an arXiv paper.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Changhai Man, Joongun Park, Hanjiang Wu, Huan Xu, Srinivas Sridharan, Tushar Krishna ·

    Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs

    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 f…

  2. X — Together (inference / OSS) TIER_1 Deutsch(DE) · togethercompute ·

    9/ ParallelKernelBench: Benchmarking LLMs on Multi-GPU Kernel Generation

    9/ ParallelKernelBench: Benchmarking LLMs on Multi-GPU Kernel Generation Paper: https://t.co/6KWFkMxEx5

  3. dev.to — LLM tag TIER_1 English(EN) · Manoranjan Rajguru ·

    Beyond Single-GPU LLM Serving: Building a Distributed vLLM Stack with Tensor Parallelism, RDMA, and Multi-Model Fusion in 2026

    <blockquote> <p><strong>Meta Description:</strong> Learn how to build a production-grade distributed vLLM inference stack in 2026 — covering Tensor Parallelism, RDMA (RoCE v2), HuggingFace Jobs, and Semantic Router Fusion for multi-model serving.</p> </blockquote> <p><a class="ar…

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