Llama 3.2 90B Vision

Processes both text and image inputs simultaneously within a 128K token context window, enabling extended visual reasoning tasks that require maintaining state across multiple images and lengthy textual analysis. Built on a Llama 3.1 70B text backbone augmented with a vision encoder component that converts image data into token embeddings compatible with the transformer architecture, allowing unified attention mechanisms across modalities.

Achieves top performance on visual reasoning tasks including spatial relationships, object interactions, and scene understanding as measured against open-weight model benchmarks. The model leverages the 70B text backbone's reasoning capabilities combined with vision encoder embeddings to perform multi-step visual inference without external tools, enabling direct comparison against other open models on standardized evaluation sets.

Supports integration with retrieval-augmented generation (RAG) systems and tool-calling frameworks with built-in safety features for preventing misuse in agent applications. The model can be integrated with function-calling interfaces and knowledge bases while maintaining safety guardrails that prevent harmful outputs or tool misuse.

Achieves performance competitive with OpenAI's GPT-4V on many vision-language tasks, positioning it as a capable open-weight alternative to proprietary vision models. The model's 90B parameter size and vision encoder design enable comparable reasoning and understanding on visual content without relying on proprietary APIs.

Outperforms Anthropic's Claude 3 Haiku model on image understanding tasks, demonstrating stronger visual reasoning capability than smaller proprietary alternatives. The larger parameter count and specialized vision encoder enable more sophisticated image analysis than lightweight models optimized for efficiency.

Maintains API compatibility with Llama 3.1 70B text model while adding vision input support, enabling existing Llama 3.1 deployments to upgrade to multimodal capability without changing application code. The model preserves text-only inference paths for backward compatibility while extending the interface to accept image inputs.

Includes optimizations for Arm-based processors and mobile hardware, enabling deployment on Qualcomm and MediaTek chipsets through ExecuTorch. The model supports device-specific operator fusion and quantization strategies that reduce memory footprint and latency on mobile platforms while maintaining inference quality.

Interprets charts, graphs, and data visualizations by analyzing visual structure, axis labels, legends, and data point relationships to extract quantitative insights and answer questions about trends, comparisons, and anomalies. The vision encoder processes the visual layout while the text backbone performs semantic reasoning about the data relationships, enabling both visual parsing and numerical inference in a single forward pass.

Analyzes documents containing mixed text and images (PDFs, scanned documents, reports) by maintaining coherent understanding across pages and sections within the 128K context window. The model processes both OCR-able text and visual elements (diagrams, photos, charts) simultaneously, enabling document-level comprehension without requiring separate preprocessing pipelines for text extraction and image analysis.

Provides instruction-tuned variants that follow user directives for vision-language tasks through supervised fine-tuning on instruction-following datasets. The model learns to interpret task specifications (e.g., 'extract all prices', 'describe in bullet points', 'answer in JSON') and adapt output format accordingly, enabling more reliable task-specific behavior than base model inference.

Enables custom fine-tuning of the 90B vision model using Meta's torchtune framework, which provides distributed training abstractions, memory optimization, and checkpoint management for adapting the model to domain-specific tasks. The framework handles multi-GPU synchronization, gradient accumulation, and mixed-precision training to make fine-tuning accessible on typical enterprise hardware.

Supports deployment on edge devices through PyTorch ExecuTorch, which converts the model to optimized bytecode and enables inference on mobile and embedded systems with reduced memory footprint. The framework handles quantization, operator fusion, and device-specific optimizations to make the model practical for on-device inference where cloud connectivity is unavailable or undesirable.

Enables single-machine inference through Ollama, which provides a simplified interface for running the model locally with automatic model downloading, quantization, and memory management. Ollama abstracts away multi-GPU orchestration complexity and provides a REST API for integration with applications, making local deployment more accessible than raw PyTorch inference.

Available through Llama Stack distributions that provide pre-configured deployments for single-node, on-premises, cloud, and on-device environments. Each distribution includes the model, inference runtime, and integration templates for common platforms (AWS, Azure, Google Cloud), reducing deployment configuration burden and enabling consistent model behavior across infrastructure types.

Provides immediate access to the model through Meta's AI smart assistant interface, enabling users to test vision-language capabilities without local deployment or API key setup. The assistant handles model inference on Meta's infrastructure and provides a conversational interface for exploring the model's capabilities on images and text.