DeepInfer is the inference framework DeepSeek published on GitHub, under the organisation name deepseek-ai and the project name DeepInfer, licensed under Apache 2.0. It describes itself as the inference framework behind DeepSeek's large-scale production services, and at release it supports only one model: DeepSeek-V4.1-Flash. The verdict first: if you want to study how DeepSeek does large-scale inference, it is worth reading; if you want to take it and serve traffic directly, the missing pieces are yours to fill in.
What the official repository contains
The repository includes kernels DeepSeek had already open-sourced, including FlashMLA, DeepGEMM, and DeepEP, plus new kernels written in TileLang; the speculative decoding scheme is called DSpark, and structured output is supported. The framework grew out of vLLM and also draws on ideas from SGLang and TensorRT-LLM. Hardware support covers exactly two kinds: NVIDIA SM100 data-centre GPUs of this generation, and Huawei Ascend 950. The environment requirements are specific: Linux, GCC 13 or newer, Python 3.12, CUDA 13 on the NVIDIA route, and CANN 9.2 on the Ascend route. The default weights are DeepSeek-V4.1-Flash-1017 on Hugging Face, downloaded automatically on first run.
Three things the repository does not contain
DeepSeek states plainly in the repository's own description that a complete inference service still needs three parts that were not released this time: a key-value cache system, infrastructure for splitting prefill and decode across different machine pools, and load balancing across data-parallel replicas. In other words, the repository gives you the inference core, not a ready-to-run production service. The official claims — a single cluster handling 155 trillion tokens a day and $38 of revenue per card per hour at off-peak pricing — come from DeepSeek itself and have not been independently verified; treat them as reference points, not as promises for your own deployment.
Deployment cost and real pitfalls
Start with the hardware bar: ordinary laptops and consumer GPUs are out of scope, so the starting point is data-centre GPUs or Ascend cards plus matching drivers and a build environment, which rules out local runs for most individual developers. There are three pitfalls: the project is less than a month old, so documentation and versions are still changing fast and copying an old tutorial may simply fail; only the V4.1-Flash model is supported, so switching models means finding another framework; and filling in the missing cache and load-balancing parts requires reading the whole codebase. It suits engineers and teams running inference services who want to learn production-grade inference; it does not suit people who only want to call a model — DeepSeek's API or plain vLLM is far less trouble.
