AI BriefingDeepInfer: High-performance inference framework dedicated to DeepSeek V4.1-Flash
deepseek-ai/DeepInfer
About the project
The inference engine that powered DeepSeek's large-scale production services has been open-sourced. It currently provides offline inference capabilities for the DeepSeek-V4.1-Flash model. It is distributed under the Apache 2.0 license and supports NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs.
It features custom-optimized kernels such as FlashMLA, DeepGEMM, and DeepEP to boost performance. It accelerates decoding speeds through DSpark-based reliability scheduling verification and SWA finite replay. Additionally, it applies asynchronous scheduling by default, which overlaps CPU batch scheduling with device execution to minimize overhead.
It supports batch-invariant inference and deterministic sampling to prevent results from varying based on batch configuration. It also provides structured output capabilities that constrain generated results using regular expressions, JSON schemas, and EBNF grammars. The architecture is based on vLLM, incorporating ideas from SGLang and TensorRT-LLM.
The KV cache system, VE/Prefill/Decode disaggregation, and load balancing are not included in this release. These features are already implemented within the engine but require separate integration for a complete service configuration. It is suitable for developers who need large-scale offline batch processing or inference of DeepSeek models on specific hardware environments.
deepseek-ai/DeepInfer
DeepInfer: High-performance, large-scale inference framework for DeepSeek models
Python
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