LLaVA Llama 3 (8B)

Processes images and text together by encoding images through CLIP-ViT-Large-patch14-336 vision encoder, projecting visual features into Llama 3's token space, then performing joint reasoning across both modalities. The architecture chains image embeddings directly into the LLM's attention mechanism, enabling the 8B Llama 3 Instruct backbone to perform visual question answering, image captioning, and cross-modal analysis in a single forward pass without separate vision-language fusion layers.

Exposes the vision-language model through three integration points: (1) Ollama CLI command `ollama run llava-llama3` for interactive chat, (2) HTTP REST API on localhost:11434 with `/api/chat` endpoint accepting multipart image + text payloads, and (3) language-specific SDKs (Python `ollama.chat()`, JavaScript) that abstract HTTP calls. All interfaces support streaming token-by-token responses, enabling real-time output rendering without waiting for full generation completion.

Ollama Cloud provides managed hosting of the LLaVA Llama 3 model with three subscription tiers (Free, Pro $20/mo, Max $100/mo) that control concurrent model instances and total GPU compute time. Billing is metered by GPU seconds consumed during inference, not by token count, allowing variable-length requests to be priced fairly. Cloud deployment abstracts hardware provisioning and uses NVIDIA Blackwell/Vera Rubin GPU architectures for quantization support.

The model inherits Llama 3 Instruct's instruction-following capabilities, enabling it to follow complex multi-step prompts, maintain conversational context across turns, and adapt tone/style based on user directives. This is achieved through supervised fine-tuning on instruction-response pairs during Llama 3's training, combined with XTuner's vision-language fine-tuning that preserves instruction-following while adding visual understanding. The 8K token context window allows multi-turn conversations with image references.

Generates natural language descriptions of images by encoding the image through CLIP-ViT, projecting visual features into Llama 3's embedding space, and using the language model to generate coherent captions. The model can produce captions of varying length and detail based on prompt engineering (e.g., 'describe this image in one sentence' vs. 'provide a detailed description'). This is a direct application of the vision-language architecture without requiring specialized captioning fine-tuning.

Answers natural language questions about image content by encoding the image and question together, then using Llama 3's reasoning capabilities to ground answers in visual features. The model performs single-image VQA without requiring separate question-image alignment modules; the CLIP-ViT encoder and Llama 3 attention mechanism jointly attend to relevant image regions and question tokens. Supports open-ended questions (e.g., 'what is happening?') and factual queries (e.g., 'how many objects are in the image?').

Analyzes documents, screenshots, and diagrams by encoding visual content and using Llama 3 to extract and reason about text and layout information. While not a dedicated OCR system, the model can read text from images, understand document structure, and answer questions about content. This works through CLIP-ViT's ability to encode text-heavy images and Llama 3's language understanding, enabling tasks like form field extraction, code snippet analysis from screenshots, and document summarization.

Processes multiple images and prompts sequentially through the Ollama CLI or REST API, with streaming responses enabling real-time output collection. The model maintains state between requests (GPU memory is not released between calls), allowing efficient batch processing without repeated model loading. Streaming is implemented via chunked HTTP responses or line-delimited JSON, enabling applications to render output incrementally without waiting for full generation.

Runs the entire vision-language model locally on user hardware without requiring cloud API calls, internet connectivity, or API keys. The GGUF quantized format (5.5GB) is downloaded once and cached locally; all inference happens on-device using Ollama's optimized inference runtime. This enables privacy-preserving analysis where images and prompts never leave the user's machine, and eliminates API rate limits, latency, and per-request costs.