技术规格

系统要求

不同量化方法和上下文大小的显存要求

Llama 4 Maverick

The Llama 4 Maverick model is a natively multimodal large language model developed by Meta, released as part of the Llama 4 model family. Its primary purpose is to deliver advanced capabilities in text and image understanding, supporting a wide range of applications including assistant-like conversational AI, creative content generation, complex reasoning, and code generation. Designed for both commercial and research deployment, Llama 4 Maverick aims to provide high-quality performance with improved cost efficiency.

From an architectural perspective, Llama 4 Maverick leverages a Mixture-of-Experts (MoE) design, a significant departure from previous dense transformer models. It comprises 400 billion total parameters, with only 17 billion parameters actively engaged per token during inference. This efficiency is achieved through the use of 128 experts, where processing involves alternating dense and MoE layers. The model integrates different modalities, such as text and images, through an early fusion mechanism, allowing for comprehensive multimodal processing from the initial stages. The internal architecture also incorporates iRoPE for managing and scaling context, further enhancing its capabilities.

Llama 4 Maverick demonstrates robust performance across diverse benchmarks, including coding, reasoning, and multilingual tasks, as well as long-context processing and image understanding. It is engineered for high model throughput and is suitable for production environments that demand low latency and precision. The model's design facilitates its deployment in scenarios requiring sophisticated multimodal interaction and efficient resource utilization, addressing modern AI application requirements.