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You can orchestrate end-to-end LLM deployment and benchmarking by using a gate-driven pipeline that coordinates stack installation, environment verification, model testing, and performance testing across multi-chip GPU backends to produce a structured JSON report.

Managing stateful data flow between LLM deployment steps requires a modular orchestration pipeline that propagates state seamlessly across install-stack, env-verify, model-verify, and perf-test stages while enforcing gate-based progress control.

Yes, you need a prepared running GPU container available before starting the LLM benchmarking pipeline, as the orchestration process assumes the environment is ready to install, verify, and test the stack directly on the multi-chip backend.

Yes, you can run individual stages like model-verify or perf-test independently due to the modular design, while each sub-skill still contributes its results to a unified final report for structured analysis.

Gate-driven control handles timeouts and errors during GPU stack verification by enforcing gate-checked progress at each stage, applying specific timeouts for every operation, and generating clear failure reporting to ensure pipeline completeness.