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Optimizing vLLM Performance through Quantization | Ray Summit 2024
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At Ray Summit 2024, Michael Goin and Robert Shaw from Neural Magic delve into the world of model quantization for vLLM deployments. Their presentation focuses on vLLM's support for various quantization methods, including FP8, INT8, and INT4, which are crucial for reducing memory usage and enhancing generation speed.
In the talk, Goin and Shaw explain the internal mechanisms of how vLLM leverages quantization to accelerate models. They also provide practical guidance on applying these quantization techniques to custom models using vLLM's llm-compressor framework. This talk offers valuable insights for developers and organizations looking to optimize their LLM deployments, balancing performance and resource efficiency in large-scale AI applications.
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In the talk, Goin and Shaw explain the internal mechanisms of how vLLM leverages quantization to accelerate models. They also provide practical guidance on applying these quantization techniques to custom models using vLLM's llm-compressor framework. This talk offers valuable insights for developers and organizations looking to optimize their LLM deployments, balancing performance and resource efficiency in large-scale AI applications.
--
Interested in more?
--
🔗 Connect with us:
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