Abstract:
A dramatic episode has recently occurred in the AI circle: an official announcement tweet by DeepSeek, before everyone had time to carefully study the technical details, first went out of the circle because of the cover text.
Some netizens discovered that the cover of the public account article "DeepSeek Open Source Inference Framework: Towards a Truly Inclusive Future" mistakenly typed "Putt & Whitney" as "HP", which happened to collide with the well-known computer brand Hewlett-Packard (HP).
Many netizens joked that DeepSeek was unintentionally sending a wave of traffic ads to HP for free; some also joked that HP executives would seize the opportunity to come to discuss cooperation.
This interesting mistake was quickly discovered by the official. The cover image has been urgently modified, and the text has been restored to "Towards a truly inclusive future." The little oolong ended successfully.
Joking aside, the weight of this article itself is the real focus of this incident. DeepSeek recently officially released a tweet announcing that its internal core inference framework DeepInfer is fully open source.
Developers who are familiar with the rhythm of DeepSeek know that this team has continued to increase its efforts in the open source track, and has previously released a number of large models, TileLang compilation tool chains, and supporting inference operator libraries.
This time, they directly disclosed the underlying inference engine that supports large-scale online services to the outside world, and presented a complete deployment plan polished in the production environment, showing their sincerity.
The official quote "do it for good and do it for everyone, do it for skill and do it for everyone" to clarify the original intention.
To put it simply, if a large model is to be implemented, not only strong model parameters are enough, but the inference framework is the key to determining throughput, latency, and computing power bills.Many small and medium-sized teams and enterprises have obtained large open source models, but they are stuck in the deployment process: high concurrent requests cannot be handled, delays fluctuate greatly, computing power overhead remains high, and the cost of privatized deployment and public cloud launch has discouraged many players.
DeepInfer is open source and aims at this pain point.
DeepSeek hopes to share the large-scale inference optimization experience accumulated in internal practice with the entire community to help developers and enterprises optimize online service indicators, increase throughput, reduce response delays, and smoothly build privatized or public cloud AI services.
After the optimization is in place, computing power overhead and energy consumption will decrease simultaneously, and the threshold for using AI services will be lowered, allowing more industries and more users to use large model services with higher cost performance, and ultimately achieve the goal of "inclusive AI".
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