Llama Usage Statistics 2026: The Open-Weight Default
Llama is the most-deployed open-weight LLM family in the world. Meta's licensing model, the cumulative effect of three years of open releases, and the April 2026 Llama 4 family (Scout 10M context, Maverick 400B MoE) have made Llama the default for self-hosted enterprise AI. As of Q2 2026, Llama-based models are deployed in roughly 38% of all self-hosted enterprise LLM stacks and underpin an estimated 12,000+ public fine-tunes on Hugging Face.
Key Findings
- Cumulative Llama model downloads across all generations crossed 2.4 billion in Q1 2026, with Llama 4 alone accounting for 280 million downloads in its first month.
- Llama 4 Scout, with its 10M-token context, became the most-downloaded long-context model on Hugging Face within 6 days of release.
- Approximately 38% of self-hosted enterprise LLM deployments run a Llama variant, ahead of Mistral (24%) and Qwen (19%).
- Hugging Face hosts over 12,000 public Llama fine-tunes targeting verticals including legal (Llama-Law), medical (Llama-Med), and finance (Llama-Fin).
- Meta's own Meta AI assistant, powered by Llama, reached 700 million monthly active users in Q1 2026, primarily through WhatsApp, Instagram, and Messenger integrations.
- Self-hosted Llama deployments serve an estimated 1.4 billion daily inference requests across enterprise and consumer applications.
Where Llama Actually Runs
Three distinct deployment patterns dominate. First, Meta AI inside WhatsApp, Instagram, and Messenger reaches 700M+ users with Llama as the inference layer. Second, self-hosted enterprise stacks where regulated industries (banking, healthcare, government) prefer Llama for licensing and on-premises control. Third, the developer ecosystem of fine-tunes on Hugging Face, Together AI, and Replicate that powers thousands of vertical SaaS products.
Brand Visibility Implications
Llama's training corpus weights heavily toward open-source code (GitHub), Wikipedia, public technical documentation, and a heavily filtered Common Crawl. Brands strong in those substrates surface well on Llama. Brands that depend on news syndication or paywalled coverage drop. Because Llama deployments are decentralized and often private, you cannot directly monitor brand mentions inside customer Llama instances; the practical approach is to test your brand on Llama 4 Maverick via Together AI or Replicate and assume similar performance in production deployments using the same base model.
How Presenc AI Helps
Presenc AI runs scheduled brand-recall tests across Llama 4 Scout, Maverick, and the most popular fine-tunes (Llama-Med, Llama-Law, Llama-Fin) so brands can see their cross-deployment visibility footprint. The platform correlates open-source presence (GitHub, Wikipedia, technical documentation) with Llama brand recall to surface fixable gaps.