Having considered the hardware components like CPU, RAM, and GPU that power local Large Language Models, the next piece of the puzzle is the operating system (OS) running on that hardware. The software tools you'll use to download, manage, and interact with LLMs need to be compatible with your OS. Fortunately, the community developing these tools generally aims for broad compatibility across the most common desktop operating systems.
Most popular tools designed for running LLMs locally offer support for Windows, macOS, and Linux. However, the setup process and sometimes performance characteristics can differ slightly between them. Let's look at what to expect for each major platform.
Windows is a widely used operating system, and developers of local LLM tools often provide dedicated installers (.exe or .msi files) to simplify setup.
macOS, especially with the advent of Apple Silicon (M1, M2, M3 chips), has become a capable platform for running LLMs locally.
Linux is a popular choice in the machine learning and development communities due to its open-source nature and flexibility.
apt
for Debian/Ubuntu or yum
/dnf
for Fedora), or compiling from source for more advanced tools like llama.cpp.The good news is that the easiest-to-use tools for getting started, such as Ollama and LM Studio which we will cover in Chapter 4, provide user-friendly installers and clear instructions for Windows, macOS, and Linux. You typically won't need deep technical knowledge of your specific OS internals to get your first LLM running with these applications.
Knowing that your operating system is likely supported allows us to move on to the next preparation steps: ensuring you have foundational software like Python available (which is often helpful, though not always strictly required by all tools) and getting comfortable with the basics of the command line interface.
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