Meta’s Llama 4 is now available on Workers AI

As one of Meta’s launch partners, we are excited to make Meta’s latest and most powerful model, Llama 4, available on the Cloudflare Workers AI platform starting today. Check out the Workers AI Developer Docs to begin using Llama 4 now.

Llama 4 is an industry-leading release that pushes forward the frontiers of open-source generative Artificial Intelligence (AI) models. Llama 4 relies on a novel design that combines a Mixture of Experts architecture with an early-fusion backbone that allows it to be natively multimodal.

The Llama 4 “herd” is made up of two models: Llama 4 Scout (109B total parameters, 17B active parameters) with 16 experts, and Llama 4 Maverick (400B total parameters, 17B active parameters) with 128 experts. The Llama Scout model is available on Workers AI today.

Llama 4 Scout has a context window of up to 10 million (10,000,000) tokens, which makes it one of the first open-source models to support a window of that size. A larger context window makes it possible to hold longer conversations, deliver more personalized responses, and support better Retrieval Augmented Generation (RAG). For example, users can take advantage of that increase to summarize multiple documents or reason over large codebases. At launch, Workers AI is supporting a context window of 131,000 tokens to start and we’ll be working to increase this in the future.

Llama 4 does not compromise parameter depth for speed. Despite having 109 billion total parameters, the Mixture of Experts (MoE) architecture can intelligently use only a fraction of those parameters during active inference. This delivers a faster response that is made smarter by the 109B parameter size.

A Mixture of Experts (MoE) model is a type of Sparse Transformer model that is composed of individual specialized neural networks called “experts”. MoE models also have a “router” component that manages input tokens and which experts they get sent to. These specialized experts work together to provide deeper results and faster inference times, increasing both model quality and performance.

For an illustrative example, let’s say there’s an expert that’s really good at generating code while another expert is really good at creative writing. When a request comes in to write a Fibonacci algorithm in Haskell, the router sends the input tokens to the coding expert. This means that the remaining experts might remain unactivated, so the model only needs to use the smaller, specialized neural network to solve the problem.

In the case of Llama 4 Scout, this means the model is only using one expert (17B parameters) instead of the full 109B total parameters of the model. In reality, the model probably needs to use multiple experts to handle a request, but the point still stands: an MoE model architecture is incredibly efficient for the breadth of problems it can handle and the speed at which it can handle it.

MoE also makes it more efficient to train models. We recommend reading Meta’s blog post on how they trained the Llama 4 models. While more efficient to train, hosting an MoE model for inference can sometimes be more challenging. You need to load the full model weights (over 200 GB) into GPU memory. Supporting a larger context window also requires keeping more memory available in a Key Value cache.

Thankfully, Workers AI solves this by offering Llama 4 Scout as a serverless model, meaning that you don’t have to worry about things like infrastructure, hardware, memory, etc. — we do all of that for you, so you are only one API request away from interacting with Llama 4. 

One challenge in building AI-powered applications is the need to grab multiple different models, like a Large Language Model (LLM) and a visual model, to deliver a complete experience for the user. Llama 4 solves that problem by being natively multimodal, meaning the model can understand both text and images.

You might recall that Llama 3.2 11b was also a vision model, but Llama 3.2 actually used separate parameters for vision and text. This means that when you sent an image request to the model, it only used the vision parameters to understand the image.

With Llama 4, all the parameters natively understand both text and images. This allowed Meta to train the model parameters with large amounts of unlabeled text, image, and video data together. For the user, this means that you don’t have to chain together multiple models like a vision model and an LLM for a multimodal experience — you can do it all with Llama 4.

We are excited to partner with Meta as a launch partner to make it effortless for developers to use Llama 4 in Cloudflare Workers AI. The release brings an efficient, multimodal, highly-capable and open-source model to anyone who wants to build AI-powered applications.

Cloudflare’s Developer Platform makes it possible to build complete applications that run alongside our Llama 4 inference. You can rely on our compute, storage, and agent layer running seamlessly with the inference from models like Llama 4. To learn more, head over to our developer docs model page for more information on using Llama 4 on Workers AI, including pricing, additional terms, and acceptable use policies.

Want to try it out without an account? Visit our AI playground or get started with building your AI experiences with Llama 4 and Workers AI.