← Anthropic Interview Insights
The high-level design part was fine but then they drilled into the CPU/GPU imbalance scenario and I kind of fumbled.
Start by clarifying the API's purpose and the workload characteristics (e.g., inference serving with GPU-bound models). Then explain how to decouple CPU and GPU scaling by monitoring GPU utilization and queue depth, and describe a scaling strategy that scales GPU resources independently while keeping CPU resources minimal.
Pro tip: Emphasize that CPU idleness is expected in GPU-heavy workloads, so scaling decisions should be driven by GPU metrics and end-to-end latency, not CPU utilization. Mention the importance of avoiding over-provisioning CPUs to save cost.
Ask about the API's purpose, expected traffic patterns, latency SLOs, and whether it's for training or inference. This sets the context for scaling decisions.
Outline a simple API (e.g., REST or gRPC) that accepts requests and returns results, with endpoints for health checks and metrics. Highlight that the API layer should be stateless and horizontally scalable.
Explain that in GPU-heavy workloads, the GPU is the bottleneck. Monitor GPU utilization, memory usage, and queue length, along with request latency and throughput.
Propose separate scaling policies for CPU and GPU pools. Scale GPU instances based on GPU metrics, and keep CPU instances minimal since they are mostly idle.
Use Kubernetes Horizontal Pod Autoscaler (HPA) with custom metrics for GPU scaling, and a load balancer to distribute requests. Consider batching and queuing to smooth spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.