Llama 4 Maverick

Llama 4 Maverick was officially announced in April 2025 as part of the Llama 4 family and represents a new era in the Llama ecosystem featuring a MoE architecture with 17 billion active parameters and 128 experts totaling 400 billion parameters overall.

Designed to deliver best-in-class multimodal intelligence at a competitive cost, Maverick rivals leading models like GPT-4o, Gemini 2.0, and DeepSeek V3. It can be run efficiently on a single NVIDIA H100 DGX host, making deployment accessible while maintaining top-tier performance.

The model was co-distilled from Llama 4 Behemoth, Meta’s much larger model, which helped boost reasoning, coding, and multimodal abilities without increasing inference cost. Through a careful training curriculum emphasizing challenging tasks, Maverick excels in coding, reasoning, multilingual understanding, and long-context benchmarks.

What it’s optimized for

Llama 4 Maverick specializes in:

• Multimodal understanding combining text and image inputs

• Handling large contexts (up to 1 million tokens), supporting multi-document workflows and lengthy conversations

• High-accuracy coding, reasoning, and multilingual tasks at lower operational cost

• Efficient mixture-of-experts inference enabling deployment on single high-end GPUs

• Versatile AI applications including chat, question answering, content generation, and document analysis

Typical use cases

Llama 4 Maverick excels in:

• Large-scale code comprehension, generation, and debugging

• Multimodal chatbots and AI assistants integrating visual and textual context

• Complex reasoning workflows over extended documents and data sources

• Multilingual customer support and content creation

• Interactive applications requiring fast throughput and scalability on standard GPU hardware

Key characteristics

• 17 billion active parameters with a total of 400 billion parameters via 128 MoE experts

• Supports up to 1 million input tokens for long-context usage

• Understands and generates based on both text and images

• Runs on single NVIDIA H100 DGX with reduced inference latency and serving cost

• Uses alternating dense and expert layers, online reinforcement learning, direct preference optimization, and curriculum focusing on hard reasoning and coding tasks

• Weights are released publicly, enabling developers to build and innovate on top of the Llama 4 suite

Model architecture

Llama 4 Maverick employs a state-of-the-art mixture-of-experts transformer architecture that selectively activates a subset of its 400 billion total parameters per token, improving inference efficiency and reducing serving latency.

The architecture interleaves dense and MoE layers to balance computing costs and performance across diverse tasks. Native multimodal training allows seamless integration of image and text understanding. Training leverages co-distillation techniques from the larger Llama 4 Behemoth teacher model and combines supervised fine-tuning with reinforcement learning for enhanced instruction adherence and reasoning abilities.

Why choose 1RPC.ai for Llama 4 Maverick

• Every call is directly tied to the exact model and version used, ensuring traceability and trust in your outputs

• Execution runs inside hardware-backed enclaves, so the relay can’t access or log your request

• Connect to multiple AI providers through a single API

• Avoid provider lock-in with simple, pay-per-prompt pricing

• Privacy by design with our zero-tracking infrastructure that eliminates metadata leakage and protects your activity

Summary

Llama 4 Maverick is optimized for broad, complex AI challenges requiring language, vision, reasoning, and coding across massive context windows. It offers the scalability and efficiency of MoE architectures while delivering top-tier performance competitive with industry-leading models. Its accessible weight release and well-balanced architecture make it ideal for developers and enterprises seeking powerful multimodal AI solutions capable of handling extensive, real-world tasks with cost efficiency and deployment flexibility.

An ideal choice for those needing a high-quality, efficient, long-context multimodal model deployable on industry-standard GPUs.