⚙️HardMLOps & DeploymentPREMIUM

GPU Serving & Autoscaling

Master the design of GPU serving infrastructure for LLMs with autoscaling, continuous batching, and cost optimization.

What you'll master

Model serving frameworks (vLLM, TGI, TensorRT-LLM, llama.cpp)

Continuous batching for throughput

PagedAttention (Logical vs Physical KV blocks)

GPU autoscaling strategies (Queue Depth, KV Utilization)

Multi-Instance GPU (MIG) for cost efficiency

Cold start mitigation (snapshotting, predictive scaling)

Quantization methods (FP8, INT8, AWQ, GPTQ)

Cost optimization: spot instances, multi-tenancy

Latency metrics: TTFT and TBT

Hard40 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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Unlock the full breakdown with architecture diagrams, model answers, rubric scoring, and follow-up analysis.

Code examplesArchitecture diagramsModel answersScoring rubricCommon pitfallsFollow-up Q&A

Premium includes detailed model answers, architecture diagrams, scoring rubrics, and 66 additional articles.