---
type: entity
subtype: organization
title: "Meta AI"
description: "Cited as an example of a model trained with ~10x fewer parameters than GPT, pointing toward faster/cheaper training."
resource: "https://ai.meta.com"
tags: [open-weights, llama, model-lab]
videos: [llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent, chatgpt-users-be-aware-of-this-major-weakness]
created: 2026-07-21
updated: 2026-07-21
---

# Meta AI

Meta AI is the lab behind the Llama family, the open-weight models that anchor most of Cole's open-source and cost-conscious builds. When a new Llama drops it is a headline event: "just a couple days ago meta released their latest Suite of large language models llama 3.2" ([0:00:00]). Because those weights are open, they are what Cole runs through fast providers like [Groq](./groq.md) and pull from [Hugging Face](./hugging-face.md), making Meta the practical foundation of his [Local LLMs as Agents](../../concepts/local-llms-as-agents.md) and [Provider Independence](../../concepts/provider-independence.md) work even in videos where the company itself is not the focus.

Cole also cites Meta as evidence that raw parameter count is not destiny. In his ChatGPT weakness video he points to it as a counterexample to bigger-is-better training: "we already have one example right here where meta's AI is trained with 10 times less parameters than other models like GPT" ([0:06:37]), pointing toward faster and cheaper training. The recurring question he tests is whether these efficient open models can hold their own as agents, framed head-to-head against closed rivals, "but does it beat GPT as an AI agent?" That comparison feeds directly into his [Model Selection](../../concepts/model-selection.md) guidance and his ongoing evaluation of open-weight [Tool Use](../../concepts/tool-use.md) against [OpenAI](./openai.md).

## Works with

- [Groq](./groq.md) - A fast inference provider Cole recommends (with a Llama model) when you need speed over raw power.
- [Hugging Face](./hugging-face.md) - AI platform whose blog post and AI Agents course provide the starting definition: programs where LLM outputs control the workflow to achieve a goal.

## Contrasts with

- [OpenAI](./openai.md) - Maker of GPT-4o mini (affordable-but-strong chat model) and text-embedding-3-small (recommended RAG embedding model).

## Related

- [Local LLMs as Agents](../../concepts/local-llms-as-agents.md) - Using openly downloadable, self-hosted models for agentic work, which hinges on the model's ability to do reliable function calling.
- [Local & Self-Hosted AI](../../concepts/local-ai.md) - Running open-weight models on your own hardware or cloud for privacy, cost control, and independence from API providers.
- [The Agent Loop (Reason-Act-Observe)](../../concepts/agent-loop.md) - The core agentic cycle where the LLM reasons, calls a tool, observes the result, and loops an unknown number of times until it decides to respond.
- [Tool Calling](../../concepts/tool-use.md) - Letting an LLM invoke external functions and APIs so it can take actions and fetch data beyond its parametric knowledge.
- [Model Selection](../../concepts/model-selection.md) - Choosing the right LLM per task by trading off quality, speed, and cost, often routing easy requests to cheap models and hard ones to expensive models.
- [Provider Independence](../../concepts/provider-independence.md) - Architecting agents so the underlying model, framework, or client can be swapped without a rewrite, avoiding lock-in through low-level abstractions.
- [Retrieval-Augmented Generation (RAG)](../../concepts/rag.md) - Grounding an LLM's answers by retrieving relevant documents from an external knowledge base and injecting them into the prompt at query time.

## Sources

- [Llama 3.2 is INSANE - But Does it Beat GPT as an AI Agent?](../../sources/llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent.md) - "[0:00:00] just a couple days ago meta released their latest Suite of large language models llama 3.2."
- [ChatGPT Users BE AWARE of this Major Weakness](../../sources/chatgpt-users-be-aware-of-this-major-weakness.md) - "[0:06:37] we already have one example right here where meta's AI is trained with 10 times less parameters than other models like GPT."
