This path is for you if Llama sounds like one chatbot and it is not. Parts cover what it is, the license at work, sizes for a laptop versus a server, hosted chat versus running it yourself, a first useful task, and community fine-tunes without the zoo. Start with the first part on this page.
- 1 What is Llama, Meta’s family of AI models, and how can you use it? Llama is a family of weight files plus runners and hosts, not one chat app. Name the door before you paste work data, and do not confuse Groq with Grok.
- 2 Can you use Meta’s Llama models at work? The license explained Llama at work needs the license PDF, Acceptable Use, and notice habits, not a vibes claim that open-weight means Apache. Write what you ship before the customer highlights Schedule C.
- 3 Which Llama model size fits your laptop, and which needs a server? Match Llama sizes to real RAM and VRAM. Scout-class fits many laptops; huge MoE names need servers. Write the hardware ticket from a load test, not from a parameter headline.
- 4 Should you use hosted Llama or run it yourself? Choose hosted Llama or self-host on purpose. Local files stay local only if you do not flip a cloud toggle or paste into a logged-in host tab.
- 5 First useful tasks to try with a Llama model Start Llama with bounded tasks and a verify step. Quote-check summaries, run the transform yourself, and never paste unchecked SLA numbers into a steerco slide.
- 6 Community versions of Llama: how to pick a fine-tuned model you can trust Avoid the Llama variant zoo. Keep one default Instruct stack, match the model card to the file, and remember license and Acceptable Use still follow the weights.
- 7 When Claude or ChatGPT is easier than running a Llama model yourself Choose Llama when local or open weights earn the ops cost. Choose a closed $20 seat when the job is writing and the GPU project has no owner. Put the chooser on one page.