Distributed GPU Training Platform: EffDL and MultiGPU system design

System-design style technical interview for an AI infrastructure role: design a compute platform that trains LLMs and other neural networks on idle GPUs across multiple data centers.

Аудио и материалы

Выводы и как готовиться

The core design tension is between heterogeneous idle GPU capacity and predictable distributed-training jobs.
A good answer separates control plane concerns from training runtime: API, queue, scheduler, orchestration, storage, tracking and monitoring.
Distributed training vocabulary matters: DDP, FSDP, ZeRO, data/tensor/pipeline parallelism, NCCL topology and checkpoint strategy are all part of the expected surface.

Distributed GPU Training Platform: EffDL and MultiGPU system design

Аудио и материалы

Problem framing: train large models on idle GPUs across data centers

API, queue and job scheduler design

Kubernetes orchestration and resource isolation

DDP, FSDP, ZeRO and model-parallel training strategies

S3 storage, MLflow tracking, Prometheus/Grafana and NCCL concerns

Выводы и как готовиться