DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%
DeepSeek has open-sourced DSpark, a framework that speeds up large language model inference by up to 85%. DSpark uses speculative decoding to improve speed without altering the model's output. It combines parallel drafting with sequential components and confidence-scheduled verification to enhance efficiency. DeepSeek tested DSpark on various models, achieving significant speed improvements. The release includes DeepSpec, aiding developers in training and evaluating speculative decoding draft models. While DSpark is not limited to DeepSeek models, its effectiveness depends on alignment with the target model. The release has garnered developer interest, showcasing promising gains in practical serving environments.
DeepSeek has open-sourced DSpark, a framework that speeds up large language model inference by up to 85%. DSpark uses speculative decoding to improve speed without altering the model's output. It combines parallel drafting with sequential components and confidence-scheduled verification to enhance efficiency. DeepSeek tested DSpark on various models, achieving significant speed improvements. The release includes DeepSpec, aiding developers in training and evaluating speculative decoding draft models. While DSpark is not limited to DeepSeek models, its effectiveness depends on alignment with the target model. The release has garnered developer interest, showcasing promising gains in practical serving environments.
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