DeepInfer targets large-scale inference
DeepInfer is an open-source inference framework that DeepSeek says powers its large-scale production services. The release exposes core capabilities for offline inference, and currently lists DeepSeek-V4.1-Flash as its supported model.
The framework includes optimized kernels and parallelism for attention and mixture-of-experts workloads, alongside asynchronous scheduling, draft-token-assisted decoding, deterministic sampling, and constrained structured outputs. Listed hardware includes NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. These stated features and platforms define the available scope; they do not establish compatibility with other models or accelerators.
DeepInfer is not a complete serving stack
The key limitation is that this release omits components needed for a complete inference service: a KV-cache system, prefill/decode disaggregation, and load balancing across data-parallel replicas. Teams evaluating it should distinguish the inference features already implemented from the additional engineering needed to build an operational serving stack.
The project is released under the Apache 2.0 license. Deployment also depends on specific prerequisites: Linux, GCC 13 or later, and Python 3.12; the NVIDIA path requires CUDA 13, while the Ascend path requires CANN 9.2. The supplied material does not establish a general performance figure, so teams should consult the project’s reproducible benchmark procedure and test against their own hardware and workloads.