Llama

The model weights are available to download, so you can run Llama on your own machines instead of renting someone else's cloud. That matters when data can't leave your network or when you want full control over the runtime. It also means you're responsible for the hardware and the upkeep.

Llama ships in several sizes, from small models that fit on a single consumer GPU to larger ones that need a server. You pick the size that matches your hardware and accuracy needs rather than accepting one fixed option. Smaller versions are cheaper to run. Bigger ones reason better on hard tasks, and that gap widens when the prompt gets messy or the task needs several steps of logic chained together.

Newer Llama versions understand multiple languages and can process images alongside text. You can ask about a chart, a screenshot, or a photo and get a text answer back. Support varies by version, so check the specific model card before you rely on image input.

You can fine-tune Llama on your own data, or distill a large version into a smaller one that runs cheaper. This is how teams tailor a general model to a narrow job, like classifying support tickets. It takes labeled data. It takes compute. Not a weekend project.

If you'd rather not run anything yourself, several providers serve Llama through an API. You call an endpoint and pay per token, which is simpler than managing GPUs. The trade-off is that you give up some control over where your data goes.

Popular tools like Ollama, llama.cpp, and vLLM support Llama out of the box, which means you aren't starting from zero when you want to serve it locally or wire it into an existing application without rewriting half your stack.