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Systems designed for batch-processed, non-real-time model execution and text generation.
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vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token gen
Processes large language model inference in batch mode with support for custom sampling and generation parameters.