DeepSeek and Peking University release DSpark inference acceleration framework, speed increased by 60% to 85%
To address the pain points of high inference latency and low concurrency efficiency in large models, the DSpark framework uses two innovations: semi-autoregressive candidate generation and confidence-based scheduling verification. While ensuring generation quality, it increases single-user generation speed by 60% to 85%. The framework has been deployed in the DeepSeek-V4 series preview services, and related code and models have been open-sourced on GitHub. #Large Models# AI Inference
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