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Meta said Llama passed one billion downloads in March 2025, a striking adoption milestone—but it does not mean one billion people use Llama or that it powers that many active applications. The available figures point to fast growth in downloads, model derivatives, and cloud use through 2024 and early 2025. They do not establish a comparable Llama-only total or market rank for 2026.
What the Llama adoption figures show
Meta’s milestones use different measures and scopes, so they should be read as dated company-reported indicators rather than one continuous, independently audited usage count.
| When | Meta-reported measure | What it counts—and what it does not show |
|---|---|---|
| August 2024 | Nearly 350 million Hugging Face downloads; more than 20 million downloads in the preceding month | Platform-specific downloads of Meta’s models. This is not a count of unique users, production installations, or active applications. |
| December 2024 | More than 650 million downloads of Llama and its derivatives, twice Meta’s reported level three months earlier | The scope explicitly includes derivatives, so it should not be treated as directly interchangeable with milestones whose scope may differ. |
| December 2024 | More than 85,000 community derivatives on Hugging Face, over five times the start-of-year count | A measure of ecosystem activity, not deployment quality or active use. |
| March 2025 | Llama passed one billion downloads | A milestone announced by Meta; it does not establish a billion distinct people, active installations, or production deployments. |
Meta also reported that hosted Llama token volume at major cloud partners more than doubled from May through July 2024, and that usage grew tenfold from January through July for some large partners. Those are separate hosted-use indicators, not figures that can be added to Hugging Face downloads or derivative counts. Meta’s August 2024 adoption update, December 2024 retrospective, and March 2025 milestone announcement describe these dated claims.
Why a download is not the same as adoption in production
“Adoption” can refer to several different things: downloading model files, publishing a fine-tuned derivative, sending requests through a hosted API, or deploying a model in a live product. Each indicates interest or use, but none alone proves the others. Downloads may include repeat downloads, testing, research, or experiments that never become a deployed service. A derivative checkpoint shows that someone modified or repackaged a model; it does not tell you how many people use it or whether it performs well.
That distinction matters when comparing Meta’s headline milestones. The billion-download announcement is evidence of broad reach for Llama, but the sources reviewed do not provide an independent audit of the total or a comparable Llama-only count for 2026. Meta’s figures are useful as the company’s own dated account of growth, not as a verified census of active deployments.
Who is using Llama, and what are they doing with it?
Meta has named examples from commercial and public-facing settings. It cited Spotify’s use of Llama for personalized recommendations and AI DJ commentary. Meta’s current open-source AI page also highlights examples involving journalism, healthcare-related guidance, science, and job search. These examples show possible applications; they do not measure how common those uses are across the market.
Meta’s December 2024 retrospective listed a broad group of technology and cloud partners: AWS, AMD, Microsoft Azure, Databricks, Dell, Google Cloud, Groq, NVIDIA, IBM watsonx, Oracle Cloud, Scale AI, and Snowflake. Meta said Llama was being run on-device, on-premises, and through managed cloud APIs. A partner relationship or available service is evidence of ecosystem support, not proof that every listed company—or every customer of theirs—has a production Llama deployment. Service details, regions, and pricing vary by provider and can change.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Partner statements reproduced in Meta’s August 2024 post reinforce the interest of individual vendors, but remain vendor claims. For example, Databricks CEO and co-founder Ali Ghodsi said that thousands of Databricks customers had adopted Llama 3.1 in the weeks after launch, calling it the company’s fastest-adopted and best-selling open-source model to that point. That is a specific claim about Databricks customers, not an independent industry-wide count.
How the wider open-model market changes the picture
Llama’s growth took place in a market where open-model competition kept changing. The ATOM report abstract says Chinese open models overtook US models in cumulative downloads by August 2025 and widened their lead through March 2026. That is regional, market-level context; it does not establish Llama’s current rank or a current Llama download total.
Broader open-source AI survey findings also should not be mistaken for Llama-specific results. A 2025 Linux Foundation Research study, commissioned by Meta and summarized by Meta, reported that 89% of organizations that leverage AI use some form of open-source AI. The study also found that two-thirds of surveyed organizations believed open-source AI was cheaper to deploy than proprietary models, and nearly half cited cost savings as a reason for choosing open-source AI. These findings describe respondents’ views and behavior across open-source AI, not the adoption or economics of Llama in particular. The study’s estimate that companies would spend 3.5 times more without open-source software is about open-source software broadly, not measured Llama savings. Meta’s summary of the 2025 study identifies its relationship to the research.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What builders should check before choosing Llama
Download growth can signal a healthy ecosystem, but it cannot decide whether a model suits a project. For a practical evaluation, check the release and deployment route you actually intend to use.
Quick Recap
- Release-specific license and access: Review the terms for the exact model version. “Open source” is not a guarantee that every release has identical conditions or that weights, code, and training data are all available on the same terms. Meta’s old Llama 2 GitHub repository is marked deprecated; its access instructions required accepting a license and requesting model-weight access, so it should not be used as a current setup guide.
- Capabilities and model size: Select a model based on the tasks, hardware, latency, and quality requirements of your application. Meta currently positions Llama 4 as a natively multimodal mixture-of-experts family, including Scout and Maverick; that is Meta’s product description, not an independent comparative benchmark. Meta’s current Llama page links to its model and platform materials.
- Deployment and operations: Decide whether you need on-device, self-hosted or on-premises operation, or a managed cloud API. These routes differ in infrastructure, data handling, maintenance, and provider-specific availability.
- Total cost and control: Account for compute, serving, engineering, customization, and ongoing operations. Survey findings about open-source AI costs are not proof that a particular Llama deployment will be cheaper than a proprietary alternative.
- Evidence that matches your use case: Look for dated, independent evaluations for the tasks you care about, and distinguish a vendor’s adoption examples from measured results for your workload.
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