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Containers, Images and GPU Cold Starts

GPU cold starts include scheduling, image loading, model loading and engine initialization. Measure each stage before choosing caches, warm pools or supported snapshot mechanisms.

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RELATED CONCEPTS

Inference Autoscaling and Cold Starts Serverless GPU Platforms Kubernetes GPU Scheduling Parallel Filesystems vs Object Storage GPUDirect RDMA and GPUDirect Storage Serving Engines: vLLM, SGLang and TensorRT-LLM

LESSONS THAT TEACH THIS

From Model to ServiceGive the service an identity and a reproducible home12 min→From Model to ServicePromote a tested release and keep a way back17 min→Inference EngineeringA batch job is judged at its last item, not its average throughput22 min→

PRACTICE THIS IN REAL QUESTIONS

Kubernetes, Slurm & GPU SchedulingOur GPU pods take four minutes to start on a fresh node and sometimes fail with 'no CUDA-capable device'. Walk me through both.→Kubernetes, Slurm & GPU SchedulingDesign a serverless GPU platform where a function that loads a 7B model cold-starts in under a second. Where does every second go today?→LLM Inference & ServingA new replica has to load a 70B model and serve traffic in under a minute. Where do the seconds go, and how do you get there?→AI Infrastructure System DesignDesign a serverless GPU platform: a customer deploys a function with a model and pays per second. Where does the cold start budget go?→Open-Weights Models & Serving EnginesDeploy one open-weights model on Kubernetes with vLLM. What goes in the manifest, and what breaks first?→LLM Inference & ServingDesign an autoscaler for GPU inference replicas that reacts to load without thrashing.→