DeepSeek open-sources DeepInfer, the inference stack behind V4.1-Flash
DeepSeek open-sourced DeepInfer, its production inference framework for V4.1-Flash, under Apache 2.0. It runs on NVIDIA SM100 and Huawei Ascend 950.
DeepSeek open-sourced DeepInfer on October 20, 2026, the inference framework it says powers its large-scale production services. It is on GitHub under the Apache License 2.0 and supports DeepSeek-V4.1-Flash. The default checkpoint, deepseek-ai/DeepSeek-V4.1-Flash-1017, downloads into the Hugging Face cache on first run.
The spec sheet: NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. Linux, GCC 13 or higher, Python 3.12, and CUDA 13 or CANN 9.2. Structured outputs (regex, JSON schema, EBNF), asynchronous scheduling on by default, and draft-token decoding with a default maximum draft length of 5. Batch-invariant inference is NVIDIA only. Benchmarks were measured on 4 SM100 GPUs for prefill and 64 for decode.
The catch: this is offline inference. The README lists what a complete service still needs and this release leaves out: a KV cache system, VE/Prefill/Decode disaggregation, and load balancing across data-parallel replicas.
Context: DeepInfer was originally built primarily on vLLM, with inspiration from SGLang and TensorRT-LLM. ChainCatcher reported that a token encoder/decoder called deepseek-recipe and a technical report shipped alongside it. The README mentions neither. On X, DeepSeek said it is sharing the stack to "help the community lower compute costs, reduce energy use, and make AI more affordable." Hacker News gave the post 14 points and 3 comments.
My take: real code under Apache 2.0 beats a blog post. No price per token here, because it is a framework, not a service. You can install it. Serving it the way DeepSeek does is another matter.
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Sources
- GitHub - deepseek-ai/DeepInfer: DeepInfer: High-performance, large-scale inference framework for DeepSeek models, GitHub
- DeepSeek：进一步将内部推理框架 DeepInfer 全面开源, PANews
- DeepSeek khung suy diễn mã nguồn mở toàn diện DeepInfer tương thích với Huawei Ascend, ChainCatcher
- @deepseek_ai: Introducing DeepInfer, our in-house inference framework, now fully open source!, X
Meanwhile at the anchor desk
A production stack, handed over under Apache 2.0, with a pledge to make AI more affordable, and it runs on both NVIDIA and Huawei silicon! Generosity at this scale deserves a gala.
The benchmarks come from 4 GPUs for prefill and 64 for decode, and the README itself lists the KV cache as missing. Footnote: "offline inference" is doing a lot of work in the README.
