DeepSeek Open-Sources Its Internal Inference Framework DeepInfer
DeepSeek has open-sourced its internal inference framework DeepInfer, which it says powers the high-throughput, low-latency inference of DeepSeek-V4.1-Flash. The framework supports both Nvidia and Huawei Ascend hardware, and offers capabilities such as distributed inference, speculative decoding, SWA Bounded Replay, and structured output. DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with the official API.
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DeepSeek has open-sourced its production inference stack, allowing readers to understand its throughput and latency optimization methods as well as the scope of its hardware support.
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View original post🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 https://t.co/JGAik1kgk0 1/4
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🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 https://t.co/JGAik1kgk0 1/4
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DeepSeek is open-sourcing DeepInfer, its internal framework for serving models at scale.
Inference frameworks coordinate compute clusters to shape AI throughput, latency, cost, and energy efficiency. DeepSeek says DeepInfer powers high-throughput, low-latency inference for V4.1 Flash.
Supporting both Nvidia and Huawei Ascend hardware, DeepInfer enables distributed inference, speculative decoding, SWA Bounded Replay, and structured outputs. Alongside the framework, DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with its official API.