Llama4 400B Instruct - Details

Llama4 400B Instruct is a large language model developed by Meta Llama Enterprise, featuring 400 billion parameters. It is designed for instruction following and focuses on multimodal AI capabilities using a MoE architecture with industry-leading context windows. The model's license information is not publicly available.

The Llama 4 collection of models are natively multimodal AI models that support text and image understanding through a mixture-of-experts (MoE) architecture. This architecture enables industry-leading performance in handling complex tasks. The series includes two efficient models: Llama 4 Scout with 17B parameters and 16 experts, and Llama 4 Maverick with 17B parameters and 128 experts, offering scalable efficiency for different applications.

The Llama4 400B Instruct model features 400 billion parameters, placing it in the very large models category, which are optimized for complex tasks but require significant computational resources. Its 10,240,000-token context length falls into the very long contexts range, enabling it to process and analyze extensive texts efficiently, though this demands substantial memory and processing power. Such capabilities make it ideal for advanced applications requiring deep contextual understanding and handling of lengthy inputs.

• Name: Llama4 400B Instruct
• Parameter Size: 400B
• Context Length: 10240k
• Implications: Very large models for complex tasks, very long contexts for extensive text handling, both requiring significant resources.

The Llama4 400B Instruct model offers possible applications across a range of tasks, including commercial use, research use, and assistant-like chat scenarios where its multilingual capabilities in languages like Italian, Indonesian, Tagalog, Vietnamese, Spanish, French, Portuguese, English, Thai, Arabic, Hindi, and German could support diverse interactions. Its potential uses might extend to visual reasoning tasks and image captioning, leveraging its multimodal design to process and generate content. Natural language generation and synthetic data generation could also be possible areas for exploration, while model distillation might benefit from its large parameter size. However, these possible uses require thorough evaluation to ensure alignment with specific goals and constraints.

• commercial use
• research use
• assistant-like chat
• visual reasoning tasks
• image captioning
• natural language generation
• synthetic data generation
• model distillation

The Llama4 400B Instruct model presents possible applications in areas such as commercial use, where its multilingual support for languages like Italian, Indonesian, and Spanish could enable possible interactions across global markets. Research use might benefit from its large parameter size and multimodal capabilities, offering possible insights into complex tasks like natural language generation or visual reasoning tasks. Assistant-like chat scenarios could leverage its potential to handle diverse queries, while synthetic data generation might explore possible ways to create training materials for non-sensitive projects. These possible applications require careful assessment to ensure alignment with specific needs and constraints.

• commercial use
• research use
• assistant-like chat
• synthetic data generation

The Llama4 400B Instruct model's medium q4 version requires a GPU with at least 24GB VRAM for efficient operation, though 32GB or more is recommended for smoother performance. This quantized version balances precision and speed, making it possible to run on systems with adequate cooling and 32GB+ system memory, but its 400B parameters mean it remains resource-intensive. Possible applications for this version may include tasks requiring moderate computational power, though thorough testing is essential.

• fp16, q4, q8

The Llama4 400B Instruct model features 400 billion parameters and a medium q4 quantized version that balances precision and performance, making it possible to run on systems with 24GB+ VRAM and 32GB+ system memory. Its multilingual support for languages like Italian, Spanish, and Arabic and potential applications in research, commercial use, and synthetic data generation highlight its versatility, though deployment requires careful evaluation of hardware and use cases.

Huggingface Model Page
Ollama Model Page