vLLM
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About
vLLM is a fast and efficient library designed for large language model (LLM) inference and serving, boasting state-of-the-art serving throughput and optimized memory management via PagedAttention. It supports seamless integration with popular Hugging Face models and various decoding algorithms, providing extensive flexibility in serving applications. vLLM is compatible with a wide range of hardware including NVIDIA GPUs, AMD CPUs, Intel CPUs, TPUs, and AWS Neuron. Key features include model quantization support, continuous batching, streaming outputs, and an OpenAI-compatible API server, making it suitable for both real-time and offline inference tasks. The tool also offers robust capabilities for handling multiple models and optimizing performance.
Platform
Task
Features
• multi-lora support
• openai-compatible api
• quantization support (int4, int8, fp8)
• efficient pagedattention for memory management
• streaming outputs
• prefix caching support
• cuda/hip graph model execution
• support for various decoding algorithms (e.g., beam search)
• seamless integration with hugging face models
• high-throughput serving
FAQs
How can I serve multiple models on a single port using the OpenAI API?
You need to run multiple instances of the server, each serving a different model, and have an additional layer to route incoming requests accordingly.
Which model to use for offline inference embedding?
For embedding, you might consider Llama-3-8b or Mistral-7B-Instruct-v0.3, while avoiding generation models.
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