DeepSeek has released DeepInfer, its internal AI inference framework, as open source, giving developers access to technology used to run the company’s AI models at scale while aiming to reduce computing costs and energy consumption.
Announced Tuesday, the framework powers DeepSeek V4.1 Flash and includes optimized computing kernels, distributed inference, speculative decoding, and scheduling tools designed to improve processing speed and efficiency.
Inference refers to the process of running a trained AI model to generate responses, with computing requirements representing a significant expense for companies operating large AI services.
DeepSeek said the release would help developers improve performance and lower the costs of deploying AI models, while providing insights into production techniques that major AI companies often keep proprietary.
The framework currently supports Nvidia SM100 GPUs and Huawei Ascend 950 processors, with the company planning to add support for future DeepSeek models and further optimizations.
Open source AI gains momentum as developers move toward local deployment
The announcement comes as openly available AI models gain traction among developers and businesses seeking alternatives to proprietary services from companies such as OpenAI and Anthropic.
Models released by DeepSeek, Alibaba’s Qwen, and Meta have helped expand access to advanced AI capabilities, allowing developers to deploy customized systems without relying exclusively on commercial cloud APIs.
Tools such as Ollama, LM Studio, and llama.cpp have also made it easier for users to run compatible AI models directly on personal computers and local servers, reducing dependence on external providers and offering greater control over data and operating costs.
According to Hugging Face’s 2026 analysis of open models, smaller models continue to dominate download activity because they can run on hardware accessible to most developers.
DeepSeek’s latest release extends that trend beyond model availability by making parts of its production infrastructure publicly accessible.
The company also open sourced deepseek-recipe, its token encoding and decoding component, which is designed to reproduce the processing behavior of its official API in independent deployments.
DeepInfer is available under the Apache 2.0 license, although the current release does not include every component required to operate a complete production inference service, including cache management and load balancing infrastructure.
While the framework targets large scale deployments rather than typical consumer hardware, its release gives developers access to optimization techniques that could help reduce the resources required to operate advanced AI systems.
The initiative reflects growing competition around AI infrastructure costs as open models and self hosted deployments become increasingly viable alternatives to proprietary AI services.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.