Deepseek has released its internal inference framework as open source under the name DeepInfer. According to The Decoder, the system is based on vLLM. It is meant to reduce compute costs and energy consumption when running AI models.
What an inference framework does
During training, an AI model learns from data. Inference is the step that follows: the finished model answers requests. An inference framework is the operating software for this phase. It distributes incoming requests across the available hardware, manages memory and makes sure that responses are generated quickly.
The more efficiently this layer works, the less compute time and electricity each individual response needs. For services with many simultaneous users in particular, such differences are noticeable in day-to-day operation. The fact that DeepInfer builds on vLLM means that Deepseek is not starting from scratch but has further developed an existing system for running models.
Why this matters for the market
According to the source, the Chinese company is thereby increasing the cost pressure on Western providers such as OpenAI and Anthropic. The reasoning behind this: anyone who can run a model more cheaply has more room to maneuver in the competition. If the software required for this is made public, other operators can benefit from it too.
For developers and companies that run their own models, it is worth a closer look. Open code can be inspected, adapted and built into your own workflows. How well DeepInfer works in practice, which hardware it supports and under which license terms it is released is not stated in the report. This should be clarified in the official documentation before deployment.
Whether the promised savings in costs and energy actually materialize will only become clear in your own tests with your specific combination of model and hardware.
