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Open-Weight AI Models
Open-weight models, local deployment, fine-tuning frameworks, and the Meta AI platform. Self-hosting verified, benchmarks sourced.
Llama 4 Maverick
400B MoE model with 17 active experts
Llama 4 Scout
109B efficient model for local deployment
Meta AI Studio
Custom AI agent builder for businesses
Local Hosting
Self-hosted inference via Ollama and vLLM
Llama vs GPT-4
Open-weight vs proprietary head-to-head comparison
Meta's open-weight Llama models span three generations. Llama 4 introduced Mixture-of-Experts architecture with Maverick (400B) and Scout (109B), while the developer ecosystem runs on PyTorch, Hugging Face, and self-hosted inference.
Llama 4 Maverick (400B) and Scout (109B) are available for download and self-hosting under Meta's community license. Llama 3.3 (70B) remains the most widely deployed open model for production inference.
Meta AI is integrated across Facebook, Instagram, WhatsApp, and Ray-Ban Meta smart glasses. The assistant surfaces Llama-powered responses directly in social feeds, messaging, and augmented reality contexts.
PyTorch-native with first-class Hugging Face integration. Enterprise licensing permits commercial deployment. Fine-tuning runs on LoRA, QLoRA, and Axolotl across consumer and data-center hardware.
In-depth coverage of Meta's open-weight Llama models. Architecture, self-hosting, fine-tuning, and honest comparisons with proprietary alternatives.
Llama Guard, Prompt Guard, and Code Shield: what each blocks, how to layer them, and the evasion rates independent researchers actually measured.
The 700M-MAU license, why the OSI says it is not open source, and the Kadrey v. Meta case: the fair-use win and the torrenting claim still in court.
Pretraining from 2T to 30T-plus tokens, the SFT-RL-DPO pipeline, and how to fine-tune with LoRA, QLoRA, and GGUF on your own GPU.
Llama 3.2 Vision versus Llama 4 early fusion, what the models read in an image, the no-image-generation limit, and the EU restriction.
Saturation, contamination, and the April 2025 LMArena episode: how to read Llama benchmark numbers without being fooled.
Meta Llama is the open-weights AI family powering 3.27B users across Meta apps. Llama 4 Scout brings a 10M token context window. Full model lineup, licensing, and deployment breakdown.
Meta Llama model weights are free to download, but hosting and inference have real costs. Cloud API pricing from $0.15/M tokens, self-hosting VRAM requirements, and total cost of ownership vs closed models.
Head-to-head comparison of Meta Llama 4 Maverick and OpenAI ChatGPT across benchmarks, pricing, open-source vs closed-source, privacy, and customization for developers and enterprises.
Step-by-step guide to running Meta's Llama models on your own hardware using Ollama, llama.cpp, or vLLM, including hardware requirements, quantization options, and Docker deployment.
Enterprise adoption guide for Meta Llama covering Llama Stack tooling, licensing nuances, deployment options (on-premises, AWS, Azure, GCP), fine-tuning with LoRA/QLoRA, security and compliance, and total cost of ownership.
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