By activating only the essential components of a massive AI model when needed, it is now possible to run 284B-scale super-AI models on standard laptops without high-performance GPUs.
Imagine this: only a few years ago, a super-AI that could only run on top-tier server computers costing hundreds of thousands of dollars now runs on the ordinary laptop on your desk. And it does so without a single expensive graphics card (GPU). It is as if you are carrying an entire massive library in your bag—AI technology has moved beyond the constraints of hardware and has stepped closer to us.
Why does this matter?
Until now, running a Large Language Model (LLM—AI that learns from vast amounts of data to converse like a human) required a high-performance GPU. This was a very high barrier to entry for individual developers or general users. However, developers are recently breaking down this barrier through intelligent engineering that makes efficient use of system memory (RAM). Source: David Hendrickson on X
These changes offer great opportunities for individuals who want to own their AI models directly. Since all processing can be handled within your laptop without sending data to external servers, it is now possible to build an independent AI environment free from security and privacy concerns. Source: How to Run DeepSeek Locally: Complete Setup Guide (2026)
Easy explanation: Mixture-of-Experts system
How can a model with a staggering 284 billion parameters (numerical values that act like the AI’s brain cells) run on a mere 64GB of RAM?
Simply put, it is thanks to the “efficient operation of expert groups.” DeepSeek-V4, with its 284B parameters, has a “Mixture-of-Experts (MoE)” structure. Source: Colibrì vs llama.cpp: Running DeepSeek V4 284B on a CPU Source: How DeepSeek Runs a 284B LLM on a Laptop (Run AI Locally)
Think of it like a large general hospital. Tens of thousands of doctors belong to the hospital (the model), but when a patient arrives, only the 1 or 2 specialists best suited to treat that specific illness are called in. The DeepSeek model works the same way. The entire model is not always “awake”; every time we ask the AI a question, it intelligently selects and activates only the “special expert sub-network” required for that specific question. Source: Colibrì vs llama.cpp: Running DeepSeek V4 284B on a CPU
| New inference technologies like the “Llama modchip” or “Colibrì” have been added to this. Source: David Hendrickson on X Source: How DeepSeek Runs a 284B LLM on a Laptop (Run AI Locally) These technologies help manage and arrange the model efficiently in RAM, allowing standard CPUs to handle the heavy computations that were once performed by expensive GPUs. Source: Android on X [Source: DeepSeek V4 Flash: Local RAM and Speed Tested | ComputingForGeeks](https://computingforgeeks.com/run-deepseek-v4-flash-locally/) |
A leap beyond the limits of hardware
You no longer need to force yourself to buy equipment worth tens of thousands of dollars to run massive AI. This technology is exciting because it has accelerated the era where anyone can have “their own AI” on their computer. Just as the information revolution was triggered by the advent of the personal computer, the weight reduction and optimization of AI models will become the catalyst for the popularization of AI utilization.
Current status
| Developers have recently combined modified infrastructure with tools like “llama.cpp” and succeeded in cramming a 284B parameter model into about 76GB of RAM space. [Source: DeepSeek V4 Flash: Local RAM and Speed Tested | ComputingForGeeks](https://computingforgeeks.com/run-deepseek-v4-flash-locally/) Source: Android on X Actual benchmark results show that in certain configurations, it can even generate 26 tokens (the smallest unit of text processed by AI) per second. [Source: DeepSeek V4 Flash: Local RAM and Speed Tested | ComputingForGeeks](https://computingforgeeks.com/run-deepseek-v4-flash-locally/) |
In particular, the DeepSeek V4 model is released under an MIT license, making it a highly attractive option because anyone can freely download and use it commercially without restrictions. Source: Colibrì vs llama.cpp: Running DeepSeek V4 284B on a CPU Source: How DeepSeek Runs a 284B LLM on a Laptop (Run AI Locally) However, the setup process can still be a bit complex for a general user to install with just a few clicks. Source: Run Deepseek V4 Flash Locally Full 2026 Setup Guide
What happens next?
This move to overcome hardware constraints will accelerate the era of “On-Device AI” (AI that runs on the device itself). As it becomes possible to efficiently run massive models on personal computers, we will see an increase in services that can leverage high-performance AI at all times without cloud costs. Going forward, optimization technologies that can run larger models with less memory are expected to become the core competitive edge of the AI ecosystem.
References
- David Hendrickson on X
- Weekly GPU News
- Run Deepseek V4 Flash Locally Full 2026 Setup Guide
- How to Run DeepSeek Locally: Complete Setup Guide (2026)
- [RFC / Benchmarks] Fitting 284B DeepSeek-V4-Flash in 57.61 (https://github.com/ggml-org/llama.cpp/discussions/31699)
- Run DeepSeek V4 Flash Locally with llama.cpp (2026)
- running deepseek-r1 671b on 64GB/ 128GB ram mac gives Error…
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[128GB vs 64GB M4 Max MacBook Pro Ultimate AI RAM… - YouTube](https://www.youtube.com/watch?v=Eu8oP7DD-ro) - deepseek-ai/DeepSeek-R1 · Hugging Face
- deepseek-r1
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- How DeepSeek Runs a 284B LLM on a Laptop (Run AI Locally)
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[DeepSeek V4 Flash: Local RAM and Speed Tested ComputingForGeeks](https://computingforgeeks.com/run-deepseek-v4-flash-locally/) - Android on X: “ @TeksEdge @grok give me the details! “ / X
- Because the entire model has been compressed to reduce size
- Because it utilizes a Mixture-of-Experts (MoE) structure to activate only the necessary parts
- Because the laptop's integrated GPU performance has improved
- Running a 284B model on a standard CPU and 64GB RAM without a GPU
- Improving model training speed by 100 times
- Using cloud models without an internet connection
- It is provided only as a paid service
- It is released under an MIT license, allowing free commercial use
- It can only be used for research purposes