Meta Llama
Open SourceThe open-weight model family powering the AI ecosystem.
Published 29 May 2026 · Last updated 27 September 2026
Scores
Popularity5/5
The most popular open-weight LLM family by a wide margin — backbone of the open-source AI ecosystem, most downloaded model family on Hugging Face, ~59K GitHub stars, and the default choice for self-hosted AI deployments.
Learning Curve3/5
Using Llama via a managed API (Groq, Together AI, Bedrock) is as straightforward as any REST API. Self-hosting is significantly harder: choosing the right quantisation, setting up vLLM or Ollama, managing GPU memory, and configuring serving all require infrastructure expertise.
Flexibility5/5
Unmatched flexibility: download weights, fine-tune on proprietary data, deploy on any hardware (from a MacBook with Ollama to a multi-GPU cluster), integrate with any framework, or call from any of dozens of API providers. No vendor controls your access or pricing.
Performance4/5
Llama 3.1 405B and Llama 4 Maverick are competitive with frontier proprietary models on most benchmarks. Llama 4 Scout and 8B/70B variants punch above their weight for their parameter counts. However, the very top of the performance leaderboard is still held by closed models.
Portability5/5
Maximum portability: run locally on CPU with llama.cpp, on any GPU cloud, via any of 10+ managed API providers, or even in-browser with WebGPU. No vendor lock-in whatsoever.
About Meta Llama
Llama is Meta's family of open-weight language models and has long been the most widely used base for open-model fine-tunes and tooling. The current generation, Llama 4, has two natively multimodal mixture-of-experts models: Llama 4 Scout, with 109B total and 17B active parameters and a context window of up to 10M tokens, and Llama 4 Maverick, with 400B total and 17B active parameters and a 1M-token context. Earlier Llama 3.x releases remain widely deployed, including small 1B and 3B models for edge devices.
Weights are released under the Llama Community License, which allows commercial use but is not open source by the OSI definition: it carries attribution and naming requirements, an acceptable-use policy, and a separate licence requirement for services above 700M monthly active users. Meta runs no paid hosted API for Llama (its Llama API preview was retired in July 2026), so access is either self-hosted through Ollama, llama.cpp, vLLM, or Hugging Face Transformers, or managed through AWS Bedrock, Azure AI Foundry, Google Vertex AI, Groq, Together AI, Fireworks AI, and DeepInfra.
Meta's own direction has shifted. In April 2026, Meta Superintelligence Labs launched Muse Spark, a proprietary, closed-weight model family that now powers the Meta AI assistant in place of Llama. Meta has said existing Llama models will stay available as open source but has not confirmed new Llama-branded releases, which makes Llama 4 a stable, well-supported choice with an uncertain roadmap.
Key Features
- Llama 4 Scout: 109B total / 17B active MoE, up to 10M-token context
- Llama 4 Maverick: 400B total / 17B active MoE, 1M-token context
- Native text and image input in both Llama 4 models
- Smaller Llama 3.x models down to 1B parameters for edge devices
- Open weights under the Llama Community License
- Self-hostable via Ollama, llama.cpp, vLLM, and Hugging Face Transformers
- Managed inference on Bedrock, Azure AI Foundry, Vertex AI, Groq, Together AI, Fireworks AI, DeepInfra
- No first-party hosted API from Meta
Pros
- No per-token cost when self-hosting, so economics scale well for high-volume workloads
- Runs on a laptop with Ollama, a server with vLLM, or almost any managed cloud API
- Largest open-weight ecosystem of fine-tunes, quantisations, and tooling
- Llama 4 Scout's 10M-token context is among the longest of any open-weight model
- Fine-tunable on domain data without vendor permission
- Available from virtually every cloud and inference provider
Cons
- Self-hosting needs GPU hardware, infrastructure knowledge, and ongoing maintenance
- The Llama Community License is not OSI open source and requires a separate licence above 700M monthly users
- Full-precision Llama 4 needs significant GPU memory
- Meta's pivot to its closed Muse models leaves the Llama roadmap uncertain
- Multimodal Llama 3.2 weights are not licensed for EU-headquartered companies
- Trails current frontier models from OpenAI, Anthropic, and Google on the hardest reasoning tasks
Meta Llama Pricing
Open SourceTech Stacks with Meta Llama
Self-Hosted AI with Ollama and Open WebUI
InfrastructureA private, ChatGPT-style assistant on hardware you own. Ollama runs open-weight models such as Google Gemma or Qwen, and Open WebUI gives them a chat interface in the browser with accounts, document search, and model management. Both run in Docker, and nothing leaves your machine.
Tools Related to Meta Llama
Works well with Meta Llama(5)
Unsloth provides optimized, memory-efficient fine-tuning support for Llama models, one of its most common training targets.
Ollama can pull and run Meta Llama's open-weight models locally with a single command, exposing them through its OpenAI-compatible API.
Groq serves Meta's Llama models, including Llama 3.1 8B and Llama 3.3 70B, as a flagship hosted family on its LPU hardware.
LM Studio downloads quantized Llama builds sized for the machine's memory and runs them locally through llama.cpp, or MLX on Apple Silicon.
vLLM serves Meta's Llama models from their Hugging Face weights behind an OpenAI-compatible API, the usual route for running Llama at production throughput.
Alternatives to Meta Llama(11)
Llama has the broadest ecosystem, from 1B edge models to Llama 4 MoE; MiniMax M3 is a single large model with a 1M-token context and native multimodal input. Llama for tooling and size range, MiniMax for long-context agents.
Meta Llama and DeepSeek are the two most widely adopted open-weight LLM families — both release weights under permissive licences and are available via Ollama, vLLM, and major cloud APIs. DeepSeek-R1 directly competes with Llama 405B on reasoning benchmarks; Llama 4 Scout introduces a 10M-token context window.
Meta Llama and Qwen are leading open-weight LLM alternatives — both offer a wide range of model sizes and are widely used with Ollama and vLLM. Qwen excels at Chinese-English bilingual tasks; Llama has the larger Western ecosystem and fine-tune community.
Meta Llama and Mistral are prominent open-weight LLM alternatives — both release Apache 2.0 or similarly permissive models, support Ollama/vLLM, and are available on major cloud providers. Mistral is Europe's leading open LLM lab with strong multilingual (EU languages) coverage.
Meta Llama and Google Gemma are open-weight LLM alternatives from two of the world's largest AI labs — both designed for self-hosting and available via Ollama. Gemma 4 (Apache 2.0) excels at small model efficiency; Llama covers a wider range of sizes up to 405B+.
Llama is the most widely supported open family, from edge sizes to Llama 4 MoE; Xiaomi MiMo is an MIT-licensed 1T MoE focused on agentic and omnimodal tasks. Llama for ecosystem support, MiMo for a permissive frontier-scale model.
