A few words on DS4
TL;DR · AI Summary
DS4 is a local AI model based on DeepSeek v4 Flash, which has rapidly gained popularity due to its efficiency and usability.
Key Takeaways
- DS4 uses 2/8-bit quantization, requiring only 96GB RAM to run.
- The author believes DS4 will become the recommended local AI model, potentially
- The project plans include quality benchmarks, coding agents, and distributed inf
Outline
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Introduces the rapid popularity of the DS4 project and its background.
DS4 uses 2/8-bit quantization, allowing it to run with minimal memory.
The project will expand into multiple specialized models and add distributed inference features.
The author invested significant time in the early development phase.
DS4 is the first AI tool that makes the author willing to use a local model for important tasks.
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- DS4 项目与本地 AI 发展
- 技术基础
- 2/8 bit 量化技术
- DeepSeek v4 Flash 模型
- 应用前景
- 本地 AI 模型替代云端服务
- 专业模型分支（如 coding / legal / medical）
- 未来方向
- 质量基准
- 编码代理
- 分布式推理
Highlights
Key sentences worth saving and sharing.
DS4 uses 2/8-bit quantization, requiring only 96 or 128GB RAM to run.
The author believes DS4 will become the recommended local AI model, potentially expanding into multiple specialized versions.
The project plans include quality benchmarks, coding agents, and distributed inference.
antirez 8 hours ago. 67664 views. I didn’t expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the experience produced by the local AI movement in the latest years, that can be leveraged more promptly because of GPT 5.5 (otherwise you can’t build DS4 in one week — and even with all this help you need to know how to gently talk to LLMs).
The last week was funny and also tiring, I worked 14 hours per day on average. My normal average is 4/6 since early Redis times, but the first few months of Redis were like that.
So, what’s next? Is this a project that starts and ends with DeepSeek v4 Flash? Nope, the model can change over time. The space will be occupied, in my vision, by the best current open weights model that is *practically fast* on a high end Mac or “GPU in a box” gear (like the DGX Spark and other similar setups). I bet that the next contender is DeepSeek v4 Flash itself, in the new checkpoint that will be released and, hopefully, a version specifically tuned for coding, and who knows, other expert-variants (not in the sense of MoE experts) maybe. For local inference, to have a ds4-coding, ds4-legal, ds4-medical models make a lot of sense, after all. You just load what you need depending on the question.
It is the first time since I play with local inference (I play with it since the start) that I find myself using a local model for serious stuff that I would normally ask to Claude / GPT. This, I think, is really a big thing. It is also the first time that using vector steering I can enjoy an experience where the LLM can be used with more freedom. DeepSeek v4 Flash is really an impressive model, no doubt about that. If you can imagine in your mind the small good local model experience as A, and the frontier model you use online as B, DS4 is a lot more B than A. I can’t wait for the new releases, honestly (btw, thank you DeepSeek).
So, after those chaotic first days, I hope the project will focus on: quality benchmarks, potentially adding a coding agent that is also part of the project, a hardware setup here in my home that can run the CI test in order to ensure long term quality, more ports, and finally but as a very important point: distributed inference (both serial and parallel).
For now, thank you for all the support: it was really appreciated :) AI is too critical to be just a provided service.