Llama 4 deployment priorities for local inference

Efficiency through mixture of experts

I recommend Llama 4 Scout for businesses that require high efficiency on limited hardware. Meta designed Llama 4 Scout with 17 billion active parameters and 109 billion total parameters. This model fits on a single NVIDIA H100 GPU with 4-bit quantization. The mixture-of-experts architecture activates only 16 experts per token. Llama 4 Maverick uses 128 routed experts and 1 shared expert to achieve high performance. Llama 4 Maverick provides 17 billion active parameters and 128 experts, which permits the model to beat GPT-4o and Gemini 2.0 Flash across many benchmarks and maintains a high performance to cost ratio for enterprises. Meta developed these models using distillation from Llama 4 Behemoth. Behemoth contains 288 billion active parameters and 16 experts. While Behemoth remains in training, it achieved 390 TFLOPs per GPU with FP8 precision. Meta used MetaP hyperparameter tuning to set per-layer learning rates and initialization scales. This technique ensures better generalization across model width and depth. The training process for the Llama 4 family used more than 30 trillion tokens. This data mixture included text, image, and video datasets.

Context depth and model performance

Llama 4 Maverick delivers high performance across text and image understanding. It achieves an ELO of 1417 on LMArena for chat. Maverick outperforms GPT-4o and Gemini 2.0 Flash on coding, reasoning, and image benchmarks. It also beats Gemini 2.0 in MathVista and MMLU-Vision. Maverick costs between $0.19 and $0.49 per 1 million tokens. The Scout model provides a 10 million token context window. This exceeds the 2 million token window of Gemini 2.0. The Scout model also beats Gemma 3 and Mistral 3.1 on several benchmarks. Both models use early fusion to combine text and vision tokens. This architecture allows the model to reason across modalities. Maverick shows 85-92% accuracy on fields requiring complex logic in Box AI evaluations. Maverick also achieves 80.5 in MMLU Pro and 69.8 in GPQA Diamond. Maverick outperforms Llama 3.3 70B in both speed and quality. The Scout model allows for reasoning over massive codebases because of its 10 million token window. This capability exceeds the 128k token limit seen in some third-party implementations. Maverick is a choice for building production-grade assistants with improved instruction following.

Control and governance in local environments

Enterprises download Llama 4 weights to run models on their own infrastructure or through managed providers. This choice allows organizations to keep data on-premises and fine-tune models on proprietary data. You already know that proprietary APIs send data to third parties. Self-hosting shifts responsibility for security, monitoring, and auditing to the deploying organization. Organizations face challenges such as shadow AI proliferation and missing audit trails when they deploy these models. Llama 4 Maverick fits in a single NVIDIA H100 DGX host. Llama 4 Scout works on a single NVIDIA H100. Some developers criticized Meta because the LMArena version differed from the version available for download. Will Meta release a reasoning model to address developer concerns about agentic capabilities? Organizations must manage identity systems to recognize autonomous agents as distinct principals. They also require monitoring to track prompts and tool calls throughout the interaction lifecycle. Using Llama 4 requires organizations to manage credentials and access control to prevent unauthorized access. Developers can deploy Llama 4 Scout on 8x H100 GPUs with a 200k token context window. They can also use 4x H100 GPUs to reduce costs with a lower context window capacity. Maverick requires 8x H200 GPUs for a 100k token context window.