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Architectures designed to handle large volumes of concurrent inference requests with low latency.
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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
Scales large language model inference to handle high volumes of concurrent requests with minimal latency.