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Kuaishou Tech

Nov 21, 2024 · Artificial Intelligence

Best Practices for Training Large Language Models on Ultra‑Large Scale Clusters

This article summarizes the challenges of distributed training for massive language models and presents a suite of solutions—including DP/TP/PP overlap, context parallelism, efficient recomputation, and a performance‑aware cost model—that together boost training throughput by over 30% on large GPU clusters.

GPU clustersLarge Language Modelsactivation rematerialization

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Best Practices for Training Large Language Models on Ultra‑Large Scale Clusters

Architects' Tech Alliance

Sep 8, 2024 · Artificial Intelligence

Design and Architecture of Multi‑Million GPU Clusters for Large‑Scale AI Model Training

The article surveys the network architectures and congestion‑control techniques used in massive GPU clusters—such as Byte’s megascale, Baidu HPN, Alibaba HPN7, and Tencent Xingmai 2.0—highlighting how high‑bandwidth, low‑latency designs and advanced RDMA technologies enable training of trillion‑parameter multimodal AI models.

AI infrastructureGPU clustersHPN

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Design and Architecture of Multi‑Million GPU Clusters for Large‑Scale AI Model Training

Architects' Tech Alliance

May 23, 2024 · Cloud Computing

Design and Comparison of High‑Performance Cloud Data Center Networks for AI Computing

This article analyzes traditional cloud data center network limitations for AI workloads and compares various high‑bandwidth, low‑latency architectures—including two‑layer and three‑layer fat‑tree designs, InfiniBand, and RoCE—providing best‑practice recommendations for building scalable, non‑blocking AI‑Pool networks.

AI computingFat TreeGPU clusters

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Design and Comparison of High‑Performance Cloud Data Center Networks for AI Computing

Tencent Cloud Developer

Mar 22, 2023 · Artificial Intelligence

Tencent Star Network: High‑Performance GPU Cluster Architecture for Large‑Scale AI Model Training

Tencent’s Star Network delivers a 1.6 Tbps Ethernet‑RDMA fabric, fat‑tree topology supporting up to 4 K GPUs, multi‑track traffic aggregation and adaptive heterogeneous links plus a custom TCCL library, cutting AllReduce overhead from 35 % to 3.7 %, speeding AI training iterations by 32 % while automating deployment and providing sub‑second self‑healing.

AI trainingGPU clustersRDMA

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Tencent Star Network: High‑Performance GPU Cluster Architecture for Large‑Scale AI Model Training

Baidu Geek Talk

Mar 21, 2023 · Artificial Intelligence

Infrastructure Challenges and Solutions for Large‑Scale AI Model Training

The article explains how the massive compute and storage demands of today’s large language models create a “compute wall” and “storage wall,” and describes Baidu Intelligent Cloud’s four‑layer full‑stack infrastructure—combining advanced parallelism techniques, optimized GPU networking, static‑graph compilation, and cost‑model‑driven placement—to train trillion‑parameter models efficiently.

AI infrastructureGPU clustersLarge Language Models

0 likes · 27 min read

Infrastructure Challenges and Solutions for Large‑Scale AI Model Training