Add usage data section
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## Usage Data Collection
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The metrics and insights throughout this post are powered by vLLM's [usage system](https://github.com/vllm-project/vllm/blob/main/vllm/usage/usage_lib.py), which collects anonymized deployment data. Each vLLM instance generates a UUID and reports technical metrics including:
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* Hardware specs (GPU count/type, CPU architecture, available memory)
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* Model configuration (architecture, dtype, tensor parallelism degree)
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* Runtime settings (quantization type, prefix caching enabled)
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* Deployment context (cloud provider, platform, vLLM version)
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This telemetry helps prioritize optimizations for common hardware configurations and identify which features need performance improvements. The data is collected locally in `~/.config/vllm/usage_stats.json`. Users can opt out by setting `VLLM_NO_USAGE_STATS=1`, `DO_NOT_TRACK=1`, or creating `~/.config/vllm/do_not_track`. The implementation details and full schema are available in our [usage stats documentation](https://docs.vllm.ai/en/latest/serving/usage_stats.html).
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---
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## Join the Journey
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## Join the Journey
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vLLM's 2024 journey demonstrates the transformative potential of open-source collaboration. With a clear vision for 2025, the project is poised to redefine AI inference, making it more accessible, scalable, and efficient. Whether through code contributions, attending [vLLM Office Hours](https://hubs.li/Q02TFDTT0), or adopting vLLM in production, every participant helps shape the future of this fast-moving project.
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vLLM's 2024 journey demonstrates the transformative potential of open-source collaboration. With a clear vision for 2025, the project is poised to redefine AI inference, making it more accessible, scalable, and efficient. Whether through code contributions, attending [vLLM Office Hours](https://hubs.li/Q02TFDTT0), or adopting vLLM in production, every participant helps shape the future of this fast-moving project.
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