Llama 3.2 90B Vision

Processes images and text simultaneously within a 128K token context window, using a vision encoder integrated with the Llama 3.1 70B text backbone to perform structured visual reasoning tasks. The architecture combines image embeddings with text tokens in a unified transformer attention mechanism, enabling the model to maintain spatial and semantic relationships across both modalities throughout the full context length. This allows reasoning over multiple images, long documents with embedded visuals, and complex multi-turn conversations involving visual content.

Specializes in interpreting complex charts, graphs, and data visualizations through visual feature extraction and semantic understanding of visual elements (axes, legends, data points, trends). The model learns to extract numerical values, identify relationships between variables, and generate textual summaries or answers about chart content. This capability is claimed to achieve state-of-the-art performance on open-weight benchmarks for chart understanding, though specific benchmark names and scores are not disclosed.

Supports extended reasoning tasks over long documents and multiple images by maintaining a 128K token context window that encompasses both text and visual content. This enables processing of full research papers with embedded figures, multi-page documents with charts and tables, and complex multi-turn conversations with visual references. The unified context window prevents context switching and enables coherent reasoning across document-length inputs.

Llama 3.2 90B Vision is distributed as an open-weight model available for download from llama.com and Hugging Face, enabling unrestricted access for research, commercial use, and community development. The open-weight distribution allows inspection of model architecture, weights, and behavior, supporting transparency and enabling community contributions. This contrasts with closed-weight proprietary models and enables self-hosting without API dependencies.

Performs end-to-end document analysis by combining optical character recognition (OCR) capabilities with semantic understanding of document layout, structure, and content. The model processes scanned documents, PDFs rendered as images, and forms to extract text, understand spatial relationships between elements, and answer questions about document content. This integrates visual understanding of document structure with language understanding to handle mixed-format documents containing text, tables, images, and handwriting.

Generates coherent, instruction-following text responses grounded in visual context from images. The model inherits the instruction-tuning from Llama 3.1 70B backbone while extending it to handle multimodal prompts where text instructions reference or depend on visual content. This enables tasks like image captioning, visual question answering, detailed image descriptions, and instruction-following that requires understanding both text directives and visual content simultaneously.

Provides a framework (torchtune) for fine-tuning Llama 3.2 90B Vision on custom datasets and use cases. The framework enables parameter-efficient fine-tuning methods (LoRA, QLoRA, full fine-tuning) to adapt the base model to domain-specific visual reasoning tasks. This allows organizations to customize the model's behavior, improve performance on proprietary datasets, and create specialized variants without training from scratch.

Enables deployment of Llama 3.2 90B Vision on edge devices through PyTorch ExecuTorch, a runtime optimized for on-device inference. ExecuTorch compiles the model to efficient bytecode, applies quantization and graph optimization, and provides a lightweight runtime for mobile and edge hardware. This allows running the model locally without cloud connectivity, reducing latency and enabling privacy-preserving inference on user devices.

Enables straightforward deployment of Llama 3.2 90B Vision on single machines through Ollama, a model serving framework that handles model download, quantization, caching, and inference serving. Ollama abstracts infrastructure complexity, providing a simple CLI and API for running the model locally without manual configuration of CUDA, memory management, or model loading. This targets developers and researchers who want to experiment with the model without DevOps overhead.

Llama 3.2 90B Vision is available through enterprise deployment partners (AWS, Databricks, Dell, Fireworks, Infosys, Together AI) who provide managed inference, scaling, monitoring, and integration services. These partners handle infrastructure provisioning, model optimization, API management, and operational support, enabling enterprises to deploy the model without managing underlying compute. This targets organizations requiring production-grade reliability, compliance, and support.

Llama 3.2 90B Vision is accessible for immediate testing through Meta's AI assistant interface, allowing users to upload images and ask questions without installation, API keys, or infrastructure setup. This provides a low-friction evaluation path for developers and non-technical users to assess model capabilities before committing to deployment. The assistant handles all backend inference and provides a conversational interface.

Llama 3.2 90B Vision claims state-of-the-art performance on open-weight benchmarks for visual reasoning, chart understanding, and document analysis, with stated competitive parity to GPT-4V on many vision tasks. The model is positioned as the strongest open-weight multimodal capability available. However, specific benchmark names, datasets, numerical scores, and detailed comparison methodologies are not disclosed in available documentation.