Llama 4 Open-Source Models: Deployment and Use Cases in the Meta Ecosystem
Llama 4 uses a Mixture-of-Experts (MoE) architecture that activates only 17 billion parameters per inference. As a result, Scout can run on a single NVIDIA H100
Llama 4 uses a Mixture-of-Experts (MoE) architecture that activates only 17 billion parameters per inference. As a result, Scout can run on a single NVIDIA H100 with Int4 quantization while still offering a 10-million-token context window. For enterprises and individual developers, this means that deploying a multimodal, long-context open model locally or in a private cloud no longer takes an entire GPU cluster. A single server is enough. The Llama 4 Model Family: How the Three Models Differ Meta released Llama 4 on April 5, 2025. The first public models were Scout and Maverick, along with a preview of Behemoth, which was still in training. All three are natively multimodal: text and images are fed in together during pretraining (early fusion), rather than having a vision module attached afterward. For full specifications, see Meta AI's official announcement . Llama 4 Scout: Long Context, Single-GPU Deployment Scout has 109 billion total parameters across 16 experts, with 17 billion parameters active per inference. Its standout feature is its context length: “Llama 4 Scout supports a 10-million-token context window, released April 2025” (Source: Meta AI) . At launch, that was the longest context of any open-weight model. Good fits include loading an entire codebase, hundreds of contracts, or years of customer service records in one pass, then summarizing and searching them. Llama 4 Maverick: General Chat and Multimodal Understanding Maverick also activates 17 billion parameters per inference, but it has 128 experts, about 400 billion total parameters, and a 1-million-token context window. Meta positions it as the main model for general assistants and chat applications, and estimates that “Maverick's distributed inference cost is roughly $0.19 to $0.49 per million tokens (3:1 input-to-output blend), estimated April 2025” (Source: Meta AI) . Llama 4 Behemoth: The Teacher Model Behemoth activates 288 billion parameters per inference and has nearly 2 trillion total para
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The Llama 4 Model Family: How the Three Models Differ
Meta released Llama 4 on April 5, 2025. The first public models were Scout and Maverick, along with a preview of Behemoth, which was still in training. All three are natively multimodal: text and images are fed in together during pretraining (early fusion), rather than having a vision module attached afterward. For full specifications, see Meta AI's official announcement . Llama 4 Scout: Long Context, Single-GPU Deployment Scout has 109 billion total parameters across 16 experts, with 17 billion
Why MoE Lowers the Barrier to Deployment
With MoE, compute depends on active parameters, not total parameters. Maverick has 400 billion parameters, but each token passes through only a shared expert and one routed expert. That makes the compute per inference close to that of a 17-billion-parameter dense model. Memory requirements, however, still depend on total parameters. MoE saves compute, not VRAM: every expert's weights must be loaded into memory. Typical hardware requirements for real deployments are: Scout (Int4 quantized) : Runs
Four Common Deployment Paths and When to Use Them
Choose a deployment method based on how sensitive your data is and how much traffic you expect. You don't need to build your own GPU cluster from day one. The options below are ordered from easiest to hardest. Path 1: Local Deployment with Ollama / llama.cpp Ollama is a good fit for individual developers testing a proof of concept. After installing it, run ollama run llama4:scout to start the model locally. Ollama also provides an OpenAI-compatible API endpoint (default: http://localhost:11434 )
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