DeepSeek V3-0324: An Open-Weight AI Model That’s Closing the Gap

Background – What is DeepSeek, and Who’s Behind It?

DeepSeek is a Chinese AI lab startup that has rapidly emerged as a leader in open artificial intelligence. Based in Hangzhou and led by founder and CEO Liang Wenfeng – a former hedge fund quant – the company was founded in late 2023 and quickly went from newcomer to industry disruptor within a year. Unlike the AI offerings from Western tech giants, DeepSeek’s model releases have been remarkably open. DeepSeek provides its large language models as open-weight AI systems with virtually no usage restrictions: anyone can download the model’s weights, fine-tune them, or deploy them without permission or licensing fees. This extreme openness (even relative to “open-weight” models like Meta’s Llama 2, which still have a gated license) is central to DeepSeek’s identity. It means independent researchers and companies worldwide can build on DeepSeek’s technology freely – a bold approach that “breaks the monopoly” of proprietary AI and democratizes AI innovation. Of course, it also raises concerns about misuse, but DeepSeek’s philosophy is that the benefits of openness outweigh the risks.

Ownership and affiliation: DeepSeek is a private venture – reportedly a Hangzhou-based startup whose controlling shareholder is Liang Wenfeng (also co-founder of hedge fund High-Flyer). Liang’s background in both finance and AI gave him the foresight to invest early in computing resources, and DeepSeek’s team leveraged clever algorithmic optimizations (like Mixture-of-Experts architectures and reinforcement learning) to achieve results that rival tech giants’ models at a fraction of the cost. The company’s first large language models launched in late 2024, and DeepSeek has continued to iterate rapidly. It introduced DeepSeek-V3, a general-purpose 671B-parameter MoE model, in December 2024, followed by DeepSeek-R1, a specialized “reasoning” model, in January 2025. These releases – completely free and open – shocked the AI world and even prompted some governments and institutions to ban DeepSeek’s models over security worries. However, many experts hailed DeepSeek’s arrival as a watershed moment for AI. Venture capitalist Marc Andreessen even called DeepSeek’s R1 AI’s “Sputnik moment,” comparing its impact to the 1957 satellite launch that spurred a global space race. In short, DeepSeek’s open strategy has upended conventional wisdom in AI – proving that a small lab with optimized methods can challenge (and sometimes surpass) the juggernauts, all while giving its work away to the public.

Hugging Face’s Role in Distributing DeepSeek Models

One key factor enabling DeepSeek’s global impact is the distribution channel for its models. Rather than keeping the model solely on its servers, DeepSeek partnered with the open-source AI community’s favorite platform: Hugging Face. The Hugging Face Hub hosts the DeepSeek model files, making them easy for developers worldwide to download or integrate into their applications. For example, the newly released DeepSeek V3-0324 model is available on Hugging Face, consisting of a whopping 641 GB of weight files in safetensors format. (This massive size reflects the 671 billion parameters of the MoE model.) Hugging Face’s hosting allows anyone with sufficient hardware to obtain and run DeepSeek locally. The hub listing also provides a model card detailing usage instructions and an MIT license, under which the model is released.

Hugging Face has been more than just a passive host; it has actively engaged with the DeepSeek ecosystem. The open-source AI community, including Hugging Face’s scientists, took great interest in DeepSeek’s techniques. Hugging Face even began “OpenR1,” an effort to replicate DeepSeek-R1’s recipe, hoping to further demystify its “special sauce.” Moreover, Hugging Face’s Text Generation Inference (TGI) framework is often used to serve DeepSeek models efficiently on consumer hardware. All of this means that when DeepSeek pushes out a new model version, Hugging Face helps quickly propagate it to researchers, developers, and enthusiasts. The result is an enthusiastic open-source community that can experiment with top-tier AI without needing API access or permission from Big Tech. The release of V3-0324 has been no exception – within hours of launch, developers on social media reported getting the model running at over 20 tokens/second on a single high-end workstation, thanks to community-contributed 4-bit quantizations and Hugging Face’s tooling. In short, Hugging Face has been an essential distribution partner, amplifying DeepSeek’s reach and fostering a rich ecosystem around these open-weight models.

Sources:

1. DeepSeek model weights and licensing on Hugging Face: https://huggingface.co/deepseek-ai
2. DeepSeek founder background: https://www.scmp.com/tech/tech-leaders/article/3253209/liang-wenfeng-chinese-hedge-fund-star-who-wants-democratise-ai-through-start-deepseek
3. Aider Polyglot benchmark analysis: https://github.com/aider/aider/issues/802
4. DeepSeek benchmark performance report: https://www.phind.com/blog/deepseek-r1
5. DeepSeek-R1 and V3-0324 announcements: https://www.deeplearning.ai/the-batch/deepseek-r1/
6. Claude 3.7 benchmark data from Anthropic: https://www.anthropic.com/news/claude-3-family
7. Nvidia stock crash after DeepSeek R1: https://www.barrons.com/articles/nvidia-stock-price-drop-ai-competition-1e328f2c
8. Alibaba stock performance post-Qwen and DeepSeek: https://www.cnbc.com/2025/03/06/alibaba-shares-rally-on-new-qwen-model.html
9. AWS Bedrock and DeepSeek integration: https://aws.amazon.com/bedrock/
10. Text Generation Inference by Hugging Face: https://huggingface.co/docs/text-generation-inference/
11. Marc Andreessen’s reaction to DeepSeek: https://twitter.com/pmarca/status/1758273772309452882
12. OpenR1 initiative by Hugging Face: https://huggingface.co/OpenR1