Almost a year ago, I wrote an article "Rise of Local LLMs?", which was well received. Now feels like the perfect time to revisit this idea—especially after recent events that have shown open-source AI overtaking proprietary models.
Chapter 1: AI, Global Politics, and the Battle for Control 🏛️🤖
In many ways, AI today is being treated like the atomic project at Los Alamos. If you’ve read "Supremacy" by Parmy Olson, you’ll see a familiar pattern—a group of researchers driven by the belief that they are "saving the world."
The difference? This time, it’s not governments racing to build the next atomic bomb—it’s corporations. And if history has taught us anything about the internet, it’s that money matters more than anything else. 💰
Tech giants are striving for full control. Imagine a world where only the U.S. had nuclear weapons. Would history have played out the same way? Now apply that thought to AI.
The first prototypes of any technology are always expensive and resource-intensive. But eventually, others catch up and make it cheaper and more efficient. We’ve seen this happen with space travel (🚀 SpaceX, ISRO) and now with AI—DeepSeek is proving that local models can challenge industry giants. And this is just the beginning.
DeepSeek’s Checkmate ♟️
If you haven’t been in a coma, you’ve probably heard of DeepSeek by now. But what wiped out $1 trillion from the U.S. stock market wasn’t just that it hit #1 on the Apple Store. 📉
It was something far bigger:
DeepSeek became the first open-source reasoning model. 🏆
We've seen the U.S. ban TikTok and label it a "Chinese spy app," but DeepSeek is different. If you don’t trust it? Run it locally.
Still worried about it being a Chinese spy tool? Read the research paper and train your own model.
The real shocker? Companies like OpenAI, Google, and Microsoft have spent billions on AI development, but DeepSeek’s success proves that state-of-the-art AI can be built for under $10 million without needing NVIDIA’s expensive chips. That changes everything.
What’s Next? ⏳
OpenAI has already accused DeepSeek of using its models to train their AI. That drama is just beginning. Expect to see the U.S. try everything to discredit DeepSeek’s achievement in the coming weeks.
But here’s the bigger picture:
The open-source AI community is thrilled, with notable figures like Yann LeCun from Meta and organizations such as the Linux Foundation praising the shift toward decentralized AI. Many developers and independent researchers have taken to forums and social media, celebrating the new wave of AI democratization and the potential it brings for global collaboration. We’re about to see more efficient, cheaper, and accessible models in the coming months—mark my words. 🔥
Just recently, Alibaba dropped another powerful open-source LLM:
🚀 Qwen 2.5 Max
And Moonshot AI released:
🌙 Kimi Ki 1.5
“There will be another DeepSeek by the end of this month, and many more this year in the U.S. and worldwide,”
— Jim Zemlin, Executive Director of the Linux Foundation
This is the new normal. Open-source AI isn’t going anywhere. And honestly? That’s how it should be. 🏆
The Revolution Isn’t Coming… It’s Here. 🚀
The rise of local LLMs is inevitable. Governments and corporations may try to control AI, but the open-source community has already proved that AI belongs to everyone.
Another side of the debate
WARS and AI ⚔️🤖
In "Supremacy" by Parmy Olson, the DeepMind team once feared that Google might use their AI technology for the U.S. military. At the time, there was a company-wide protest, and the project was eventually shut down.
Fast forward to today: amid the Israel-Gaza conflict, reports suggest that Google has provided AI assistance to the Israeli military. Meanwhile, AI is also being used in the Ukraine-Russia conflict, with both sides leveraging AI-driven strategies.
The reality is clear—there is no stopping AI from being used in warfare. We are already living in that future.
Check this Video for more on that
This is yet another reason why technologies like these should never be controlled by a single nation or military power. Open-source AI ensures that knowledge and advancements are distributed, rather than being hoarded for geopolitical dominance.
Chapter 2: Why Open-Source AI Models Running on Your Hardware Are the Future? 💻🚀
Privacy is the obvious reason to run AI models locally, but let’s be real—billions of users willingly share their data on Instagram, YouTube, and TikTok every day. So, it’s safe to say most people don’t actually care about privacy anymore.
But that’s not the only reason local AI is the future. Let’s explore some key factors.
1. The Cost Factor 💰
According to this article, here are the most common use cases for ChatGPT:
- 📚 Homework Assistance
- 💬 Personal Advice & Communication
- 🌍 Travel & Lifestyle
- 🔞 Sexual Content (Failed attempts 🤦♂️)
- 🖥 Coding Help
Apart from the last one (coding), do you really need a state-of-the-art reasoning model for these tasks? Absolutely not.
Instead of paying for expensive AI subscriptions, you’d be better off using smaller models locally with tools like Jan AI—completely free!
If your goal is just to get horny with AI, for god’s sake, at least use a fine-tuned model built for that. 😂
Save Money on AI Coding Assistants 🧑💻
For coding, DeepSeek R1 is now available to run locally, making it a solid alternative to GitHub Copilot or Cursor. That means you can cancel your subscriptions and still get top-tier AI assistance.
🚀 Check out this Dev.to article to set it up in Visual Studio Code—or, if you're feeling adventurous, build your own AI plugin in under 5 minutes!
2. The Environmental Impact 🌱⚡
Did you know that a single ChatGPT query consumes 10-100x more energy than a locally run LLM response? That’s because cloud-based AI models rely on massive compute clusters running 24/7.
Meanwhile, running a quantized 7B LLM on your laptop consumes just 20-50W—about the same as browsing the web.
To put it in perspective, here’s a side-by-side comparison:
🔋 Energy Consumption
| Factor | Local LLMs 🌿 | Cloud-Based LLMs ☁️ |
|---|---|---|
| Compute Power | Runs on consumer hardware (low power) | Requires massive data centers |
| Efficiency | Optimized for single-user inference | Thousands of power-hungry GPUs |
| Scalability | Less scalable but efficient | Highly scalable but wastes idle compute |
| Carbon Footprint | Lower (if optimized) | Very high due to cloud operations |
🌍 Which One is More Sustainable?
| Factor | Local LLMs (Edge AI) 🌿 | Cloud-Based LLMs ☁️ |
|---|---|---|
| Energy Efficiency | ✅ Lower power use | ❌ High due to data centers |
| Hardware Sustainability | ✅ Uses existing hardware | ❌ High GPU demand & e-waste |
| Data Privacy & Network | ✅ No internet needed | ❌ Heavy data transfer |
| Carbon Footprint | ✅ Lower | ❌ Higher (always-on servers) |
Read more on AI’s environmental impact here
So next time you use a cloud-based LLM just to role-play as a medieval knight or write a poem about your ex, maybe think twice. 😏
3. Speed & Latency 🚀
One of the biggest advantages of local AI models is speed. When you run an LLM on your own hardware, the response time is instantaneous because:
✅ There’s no internet latency – no data needs to travel back and forth to a remote server.
✅ You bypass API limits – no rate-limiting, no throttling.
✅ Your AI responds as fast as your hardware allows – no waiting on cloud server congestion.
Building Small Apps running on consumer hardware instead of relying on Cloud Apis is much faster and cheaper.
Final Thoughts
Between cost savings, privacy, and sustainability, running AI locally on your hardware isn’t just a geeky alternative—it’s the future.
I know it’s exciting, but many will argue that hardware requirements are still a bottleneck—and I won’t deny it. Not everyone can afford high-end GPUs to run these models offline. But hardware is evolving fast, and soon, this will no longer be a limitation.
Take Apple’s new Mac Mini M4—it packs an impressive AI-capable chip at a reasonable price. Meanwhile, NVIDIA is working on making its chips more efficient and accessible, knowing that the future of AI isn’t just in data centers but in everyday devices.
Soon, AI won’t just be something you access through a cloud API—it’ll be embedded in basic laptops, phones, and even IoT devices. On the other side, LLMs are becoming more optimized for today’s hardware, making it possible to run powerful AI without needing a supercomputer.
The DeepSeek story proves that the future isn’t about a single dominant AI or an AGI (Artificial General Intelligence) that does everything. Instead, it’s about specialized models working together—where you can pick and choose the best models for specific tasks and build applications on top of them.
We are entering the era of multi-modal AI systems, where applications will combine multiple specialized AI models to create something far more powerful than a single, monolithic AI.
Beyond that, we're seeing:
- On-device AI acceleration 🚀 – Smartphones, gaming consoles, and edge devices are integrating dedicated NPUs (Neural Processing Units) to run AI locally with minimal power consumption.
- Open-source hardware initiatives 🏗️ – Projects like RISC-V AI chips are working toward affordable, open AI processing units that break dependency on proprietary silicon.
- Federated AI 🌍 – A system where AI models train across multiple decentralized devices, improving efficiency while maintaining privacy.
- Energy-efficient AI ⚡ – The rise of low-power AI models designed to run on minimal hardware while achieving state-of-the-art performance.
- AI compression & quantization 📉 – Techniques that significantly reduce model size and computational requirements, making high-quality AI accessible on everyday devices.
- Community-driven innovation 🤝 – Open-source developers constantly improving and sharing models, ensuring that no single corporation can monopolize AI.
Upcoming
In the next article, we’ll explore:
🔥 Where to get started with local AI models
💡 Ideas for your own Open-Source AI project using offline models
Isn’t this exciting? What’s your take on local AI? Let’s discuss in the comments! 🌟 Do you see yourself experimenting with local AI models? Have any cool ideas in mind? Drop a comment below—I’m all ears!
If you want to discuss a project idea, need guidance, or just want to chat about AI, hit me up on Twitter @sarthology. Let’s build something awesome together! 🚀
Top comments (4)
This article is 🔥! I really enjoyed the breakdown of DeepSeek’s impact and the broader discussion on open-source AI vs proprietary models. The way you connected this to past tech revolutions, like the space race, really adds depth to the discussion, and DeepSeek’s approach truly feels like a groundbreaking shift in AI..
Excited to see where this revolution leads! Thanks for sharing such an insightful piece. 🚀🔥
Thanks Alok. 🫶
The cope from USA is so real. 😂
My favorite tweet on the levels of cope we're seeing.
Love the democratizarion of new tech!
Exactly my thoughts 🤣