LLMs don't run on a single GPU: Anatomy of AI infrastructure in 2026
This Habr article explores the architectural challenges of deploying modern 70B parameter LLMs. The author explains why a model might suffer from poor performance despite having sufficient VRAM. The material details key components of 2026 AI infrastructure: memory hierarchy (HBM, KV-cache), prefill and decode mechanisms, and the role of high-speed interconnects like NVLink and InfiniBand. Special attention is given to parallelism strategies (tensor and pipeline) and MoE (Mixture of Experts) architecture. The author analyzes how the interaction between GPUs, CPUs, RAM, and NVMe affects overall cluster throughput, helping to identify exactly where bottlenecks occur when scaling AI systems in real-world production environments.
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