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ContentUpdates and recent posts about vLLM..
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qballscholar
@qballscholar started using tool Terraform , 3 weeks, 4 days ago.
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qballscholar
@qballscholar started using tool Rancher Kubernetes Engine (RKE2) , 3 weeks, 4 days ago.
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qballscholar
@qballscholar started using tool GitLab CI/CD , 3 weeks, 4 days ago.
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qballscholar
@qballscholar started using tool Amazon Web Services , 3 weeks, 4 days ago.
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eon01
@eon01 started using tool k3s , 3 weeks, 4 days ago.
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mjh
@mjh started using tool Rust , 3 weeks, 4 days ago.
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mjh
@mjh started using tool Redis , 3 weeks, 4 days ago.
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mjh
@mjh started using tool React , 3 weeks, 4 days ago.
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mjh
@mjh started using tool Python , 3 weeks, 4 days ago.
vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.