DeepSeek’s latest inference release: Transparent open source phantom?

DeepSeek’s recent updates DeepSeek-V3/R1 inference system It is causing a buzz, but for those who value true transparency, there are many shortcomings in this announcement. While the company demonstrates impressive technological achievements, a closer look reveals selective disclosures and key omissions that have brought the promise of true open source transparency to question.

Impressive indicators, incomplete disclosure

This release highlights engineering expertise such as advanced cross-node expert parallelism, overlapping communication with computing, and production statistics that claim to deliver significant throughput – for example, billions of tokens are provided daily, and each H800 GPU node can process up to 73.7K tokens per second. These numbers sound impressive and propose a high-performance system to focus on efficiency in a slight way. However, such claims are without a systematic, reproducible blueprint. The company has provided part of the code, such as a custom FP8 matrix library and communication primitives, but key components such as custom load balancing algorithms and decomposed memory systems are partially opaque. This fragmented disclosure places independent verification in an unreachable situation, ultimately undermining confidence in the claims filed.

Open Source Paradox

DeepSeek proudly regards itself as an open source pioneer, but in practice it depicts different pictures. Although the infrastructure and some model weights are shared under permitted permissions, there is a clear comprehensive documentation for the data and training procedures behind the model. Important details (such as the dataset used, the filtering process applied, and the steps used for bias) are clearly missing. This omission is especially problematic in a community that increasingly values ​​full disclosure as a means of evaluating technical merits and ethical considerations. Without a clear data source, users will not be able to fully evaluate potential biases or limitations inherent in the system.

Furthermore, the licensing strategy deepens skepticism. Despite the open source claim, the model itself is limited by a custom license with exceptional restrictions, thus limiting its commercial use. This selective openness—sharing fewer critical parts while retaining core components—echoes a trend called “open cleaning,” where the emergence of transparency takes precedence over substantial openness.

Lack of industry standards

In an era where transparency has become the cornerstone of trusted AI research, DeepSeek’s approach seems to reflect the practice of industry giants more than the ideals of open source communities. While companies like Meta and Llama 2 also face criticism for their limited data transparency, they at least provide a comprehensive model card and detailed documentation on ethical guardrails. By contrast, DeepSeek chose to emphasize performance metrics and technological innovation while avoiding discussions about data integrity and ethical safeguards as well.

This selective sharing of information will not only leave key issues unsolved, but also weaken the overall narrative of open innovation. True transparency means not only revealing the impressive parts of your technology, but also having an honest conversation about its limitations and challenges that still exist. In this regard, the latest version of DeepSeek is insufficient.

Call for real transparency

For enthusiasts and skeptics, the commitment to open source innovation should be accompanied by a comprehensive responsibility. DeepSeek’s recent update is technically interesting, but seems to prioritize polished introductions to engineering strengths over deeper, more challenging, truly open work. Transparency is not just a checklist project; it is the foundation for trust and collaborative advancement in the AI ​​community.

A truly open project will include a complete set of documents, from the complexity of system design to the ethical considerations behind training data. It will invite independent reviews and promote an environment that reveals achievements and shortcomings. Until DeepSeek takes these extra steps, its claim to open source leadership is only partially confirmed at best.

All in all, while DeepSeek’s new inference system likely represents a leap in technology, its approach to transparency suggests a cautionary tale: impressive digital and cutting-edge technology cannot automatically equate with true openness. For now, the company’s selective disclosure reminds you that in the world of AI, there is as much transparency as you miss and as you share.