AI Infra知识全景，你掌握了吗？🔍
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本质上… The current AI infrastructure landscape has matured far beyond merely stacking GPU servers onto commodity machines. It now integrates specialized silicon such as GPUs and TPUs/NPUs, high‑speed interconnect fabrics like NVLink/R/NCCL/TI‑ONE’s RoCEv2 fabric layers, and multi‑tiered storage hierarchies ranging from cheap object buckets through warm shared filesystems down to hot local NVMe caches. All se pieces are orchestrated by modern cloud‑native platforms built on Kubernetes/TKE/TI‑ONE toger with dedicated MLOps toolchains such as Ray, PyTorch FSDP/DeepSpeed ZeRO stages, and serving frameworks such as vLLM/SGLang. Understanding this layered ecosystem is essential for anyone who wishes to design reliable training pipelines, manage massive model warehouses efficiently, or deliver low‑latency inference services at scale.
AIGC 基础设施全景——从概念到落地
The term “AI Infrastructure” refers collectively to every hardware component, network fabric, storage layer, software stack,
本质上… The current AI infrastructure landscape has matured far beyond merely stacking GPU servers onto commodity machines. It now integrates specialized silicon such as GPUs and TPUs/NPUs, high‑speed interconnect fabrics like NVLink/R/NCCL/TI‑ONE’s RoCEv2 fabric layers, and multi‑tiered storage hierarchies ranging from cheap object buckets through warm shared filesystems down to hot local NVMe caches. All se pieces are orchestrated by modern cloud‑native platforms built on Kubernetes/TKE/TI‑ONE toger with dedicated MLOps toolchains such as Ray, PyTorch FSDP/DeepSpeed ZeRO stages, and serving frameworks such as vLLM/SGLang. Understanding this layered ecosystem is essential for anyone who wishes to design reliable training pipelines, manage massive model warehouses efficiently, or deliver low‑latency inference services at scale.
AIGC 基础设施全景——从概念到落地
The term “AI Infrastructure” refers collectively to every hardware component, network fabric, storage layer, software stack,