What Is Local AI?

What local AI means

Local AI is AI inference performed on hardware you control — typically a desktop, laptop, workstation or local server — instead of sending every prompt and input to a remote model provider.

The important word is inference. Training a frontier-scale model and running an already-trained model are very different workloads. Local AI usually means downloading model weights and using a runtime such as Ollama or llama.cpp to execute those weights on your CPU, GPU or a combination of both.

“Local” also does not automatically mean that every component is offline. A local application may still download models, check for updates, call web services or use cloud features. If privacy or offline operation matters, evaluate the whole data path, not only where the model weights are stored.

The local AI stack

A useful way to understand local AI is as several layers:

1. Model — the trained weights and associated metadata.
2. Model format — for example GGUF in the llama.cpp ecosystem.
3. Runtime — software that loads the model and performs inference.
4. Hardware backend — CPU, NVIDIA CUDA, Apple Metal, AMD/other supported acceleration, depending on the runtime.
5. Interface or application — a terminal, desktop UI, editor integration, API client or your own software.

This separation matters because a model being downloadable does not guarantee that every runtime supports its architecture, format or features. llama.cpp, for example, documents GGUF as its required model format and provides conversion tooling for supported models that arrive in other formats.

Local AI vs cloud AI

Neither architecture is universally better. They optimize for different constraints.

Local inference can be attractive when you want more control over where inputs are processed, need offline operation, want to experiment with downloadable models, or expect enough repeated use that owning the compute is useful.

Cloud inference can be attractive when you want access to models that are too large for your hardware, do not want to manage GPU memory and runtimes, need elastic capacity, or prefer an API over maintaining the inference stack.

There is also a middle ground. An application can use a local model for some tasks and a remote model for others. Treat “local vs cloud” as an architecture decision, not an ideology.

What do you need to run AI locally?

At minimum you need a supported runtime, enough storage for the model, enough memory to load and execute it, and a model compatible with the runtime.

The GPU is important but it is not the only variable. System RAM, VRAM, model size, quantization, context length and runtime behavior all affect whether a setup is practical. A smaller quantized model may fit entirely in GPU memory; a larger model may require partial CPU/GPU execution or more system memory.

This is why “Can my PC run a 32B model?” cannot be answered reliably from the model's parameter count alone.

What to learn next

If you are new to local AI, use this order:

1. Hardware requirements — understand CPU, RAM, GPU and VRAM.
2. VRAM — understand what consumes GPU memory.
3. Quantization — learn why the same model appears in many sizes.
4. GGUF — understand the model format commonly used by llama.cpp.
5. Ollama — move from concepts to a practical local runtime.

This sequence prevents a common mistake: downloading a model first and only then discovering that the chosen variant does not fit the machine.

Sources

• Ollama documentation
• llama.cpp — obtaining and quantizing models
• Hugging Face — GGUF