Local AI model unlocks a new experience without network connections
Have you ever thought about using a powerful AI assistant without a network? In 2024, the craze for running large AI models locally is on the rise, allowing people to enjoy the magic of AI anytime, anywhere.
In the past, when we used AI assistants, we needed to connect to the Internet and transfer data to the cloud for processing. But now, some developers and hobbyists are starting to run large AI models on their local computers, which not only protects privacy, but also avoids network lag and responds faster.
The biggest benefit of running a large model locally is that you don't need to be connected to the Internet. Imagine you're on a plane or train, and suddenly you need an AI assistant to help you process some files or find information, but there's no internet signal around. With a large local model, you can use powerful AI capabilities anytime, anywhere.
A variety of local large models to play as you like
There are multiple ways to run large AI models locally, each with its own characteristics and suitable for different usage scenarios and needs.
The easiest way is to use the open-source framework Ollama to run open-source large language models locally, such as LLaMA and Llama2. These models, while slightly inferior to commercial models, are completely free and open-source, and the Ollama framework is easy to use, so even non-professional developers can get started quickly.
Another popular practice is to download a large pre-trained model and call it through a local client. At present, many companies and organizations have released their own large models, and users only need to pay to download them, and they can freely use them on their local computers. The advantage of this approach is that the model performance is better and the user experience is close to that of an online service.
If you're not satisfied with your existing model, you can also train a custom model and deploy it locally. By collecting domain-specific data and training specialized models, we can better meet individual needs. However, this requires certain professional knowledge and hardware resources, and the threshold is high.
Hardware strength determines the experience of large models
To run large AI models locally, hardware configuration is key. In general, the higher the performance of the memory and graphics card, the larger the model can be supported and the faster the response will be.
For ordinary users, 8GB of RAM is basically enough to run a medium-sized model with 7B parameters. If the memory reaches 16GB, it can support 13B large models, and the performance will be qualitatively improved. If you're a heavy user, or need to run multiple models at the same time, consider a 32GB or even 64GB RAM configuration.
In addition to memory, the graphics card is also a very important part. At the moment, Nvidia's GPU graphics cards are the best choice, which can greatly increase the computing power of AI models. If you have a discrete graphics card, such as a GeForce RTX 3060 or higher, you'll basically be able to run large mainstream models smoothly.
For Apple users, M-series chips are also a good choice. Apple's M1 and M2 chips are quite powerful enough to drive medium-sized AI models. The latest M2 Pro/Max and M2 Ultra have outperformed many discrete graphics cards and are fully capable of running large models.
The higher the hardware configuration, the higher the cost. However, fortunately, there are now many cheap hardware solutions, such as AMD's CPU+GPU solution, the price is relatively low, but the performance is also quite good, and it is the first choice for many enthusiasts and student parties.