{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-gemma3","slug":"gemma3","name":"Gemma 3 (2B, 9B, 27B)","type":"model","url":"https://ollama.com/library/gemma3","page_url":"https://unfragile.ai/gemma3","categories":["text-writing"],"tags":["ollama","open-source","google"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-gemma3__cap_0","uri":"capability://text.generation.language.multi.size.transformer.inference.with.quantization.aware.training","name":"multi-size transformer inference with quantization-aware training","description":"Gemma 3 provides five parameter-efficient variants (270M to 27B) trained with Quantization-Aware Training (QAT), enabling 3x memory reduction compared to non-quantized models while maintaining near-BF16 quality. Models are distributed as GGUF artifacts via Ollama, supporting both local GPU inference and cloud-hosted deployment with automatic hardware optimization for NVIDIA Blackwell/Vera Rubin architectures.","intents":["Deploy language models on resource-constrained hardware without sacrificing quality","Choose the right model size for latency vs. capability tradeoffs","Run inference locally without cloud dependencies or API costs","Leverage hardware-specific optimizations for faster inference"],"best_for":["Solo developers building local LLM agents with limited GPU VRAM","Teams deploying models on edge devices or cost-sensitive infrastructure","Builders prototyping multi-model systems requiring size flexibility"],"limitations":["Exact quality degradation from QAT vs. full-precision models is undocumented","GPU VRAM requirements per variant not specified; requires empirical testing","CPU inference possible via Ollama fallback but not officially benchmarked for Gemma 3","Inference latency/throughput metrics not published; performance varies by hardware"],"requires":["Ollama 0.6 or later","NVIDIA GPU with 2GB+ VRAM for 270M/1B variants; 8GB+ for 12B; 16GB+ for 27B (estimated)","Python 3.7+ or Node.js 14+ for SDK usage"],"input_types":["text (all variants)","images (4B, 12B, 27B variants only)"],"output_types":["text (streaming or buffered)","structured JSON via tool calling"],"categories":["text-generation-language","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_1","uri":"capability://image.visual.vision.language.understanding.for.text.and.image.inputs","name":"vision-language understanding for text and image inputs","description":"Gemma 3's 4B, 12B, and 27B variants support multimodal input combining text and images, enabling visual question answering, image captioning, and document understanding. Images are encoded alongside text tokens within the transformer's 128K context window, allowing interleaved reasoning over both modalities without separate vision encoders.","intents":["Build document understanding systems that reason over scanned PDFs or screenshots","Create visual question-answering agents that analyze charts, diagrams, or photos","Implement image captioning or alt-text generation at scale","Combine OCR with reasoning for form processing or data extraction from images"],"best_for":["Developers building document processing pipelines with local inference","Teams creating accessibility tools requiring image-to-text conversion","Builders prototyping multimodal RAG systems without cloud vision APIs"],"limitations":["Vision capability only available in 4B, 12B, 27B variants; 270M and 1B are text-only","Image input format specifications (resolution, file types, max dimensions) not documented","No benchmark data on vision performance vs. specialized vision models (CLIP, LLaVA)","Image encoding adds latency and context window consumption; exact overhead unknown"],"requires":["Ollama 0.6 or later with vision support enabled","4B variant minimum (3.3GB disk); 12B or 27B recommended for complex visual reasoning","Image files in common formats (PNG, JPEG, WebP assumed but not explicitly stated)"],"input_types":["text","images (PNG, JPEG, WebP assumed)"],"output_types":["text (natural language descriptions, answers, extracted data)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_10","uri":"capability://planning.reasoning.improved.reasoning.capabilities.with.transformer.scaling","name":"improved reasoning capabilities with transformer scaling","description":"Gemma 3 is claimed to have 'improved reasoning' compared to previous generations, implemented via standard transformer scaling (larger parameter counts, extended training) without documented architectural innovations. Reasoning improvements are claimed but not benchmarked; the mechanism is implicit in the model's training rather than explicit architectural features like chain-of-thought prompting or reasoning-specific loss functions.","intents":["Solve multi-step reasoning problems (math, logic, code generation)","Perform complex question-answering requiring inference chains","Generate explanations and justifications for decisions","Handle ambiguous or underspecified problems"],"best_for":["Developers building reasoning-heavy applications (tutoring, code generation, analysis)","Teams prototyping before investing in specialized reasoning models","Builders needing general-purpose reasoning without domain-specific fine-tuning"],"limitations":["Reasoning improvements are claimed but not benchmarked against baselines (no MMLU, GSM8K, HumanEval scores published)","No explicit reasoning prompting techniques documented (e.g., chain-of-thought, step-by-step)","Reasoning quality likely degrades on out-of-distribution problems","No comparison to specialized reasoning models (o1, Gemini 2.0 with extended thinking)"],"requires":["Ollama 0.6 or later","12B or 27B variant recommended for complex reasoning; 4B may struggle","Sufficient context window for multi-step reasoning (128K available)"],"input_types":["text (natural language problems, code, math, logic puzzles)"],"output_types":["text (reasoning steps, solutions, explanations)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_11","uri":"capability://data.processing.analysis.quantized.model.distribution.via.gguf.format","name":"quantized model distribution via gguf format","description":"Gemma 3 models are distributed as GGUF artifacts (Ollama's standard format), enabling efficient local storage and inference without requiring full-precision weights. GGUF is a binary format optimized for CPU and GPU inference; Ollama's runtime loads GGUF files and manages GPU memory allocation. Quantization-Aware Training (QAT) ensures quality parity with full-precision models while reducing disk and memory footprint by 3x.","intents":["Deploy models on machines with limited disk space or VRAM","Reduce model download time and bandwidth costs","Run multiple models concurrently on single GPU","Distribute models via CDN or offline media"],"best_for":["Developers with limited hardware resources (laptops, edge devices)","Teams distributing models offline or via bandwidth-constrained networks","Builders deploying models in containerized environments with storage constraints"],"limitations":["GGUF format is Ollama-specific; models cannot be easily ported to other inference engines without conversion","Quantization quality depends on QAT training; exact quality loss vs. full-precision not documented","GGUF loading and inference is CPU-bound for small models; GPU utilization may be low for 270M/1B variants","No support for mixed-precision inference (e.g., quantized weights with full-precision activations)"],"requires":["Ollama 0.6 or later with GGUF support","Disk space for GGUF artifacts (292MB to 17GB)","GPU with GGUF support (NVIDIA, AMD, or CPU fallback)"],"input_types":["GGUF binary files (downloaded via ollama pull)"],"output_types":["Model loaded in GPU/CPU memory, ready for inference"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_2","uri":"capability://text.generation.language.extended.context.reasoning.with.128k.token.window","name":"extended context reasoning with 128k token window","description":"Gemma 3's 4B, 12B, and 27B variants support 128K token context windows (32K for smaller variants), enabling multi-document reasoning, long-form summarization, and in-context learning with extensive examples. The extended context is implemented via standard transformer attention mechanisms without documented architectural modifications, allowing full document or conversation history to inform model outputs.","intents":["Summarize long documents (research papers, books, meeting transcripts) in a single pass","Build few-shot learning systems with dozens of examples in context","Implement multi-turn conversations with full history retention without external memory","Perform cross-document reasoning and synthesis without chunking"],"best_for":["Developers building document analysis tools requiring full-text context","Teams implementing in-context learning without fine-tuning","Builders creating conversational agents with long-term conversation memory"],"limitations":["128K context window requires proportional increase in inference latency; exact scaling unknown","Attention computation is O(n²) in sequence length; 128K tokens may cause memory pressure on GPUs with <16GB VRAM","No documented techniques for efficient long-context inference (e.g., sliding window, sparse attention)","Quality degradation at extreme context lengths (>100K tokens) not benchmarked"],"requires":["Ollama 0.6 or later","12B or 27B variant recommended; 4B may struggle with full 128K utilization","GPU with 16GB+ VRAM for sustained 128K context inference"],"input_types":["text (up to 128K tokens for 4B/12B/27B; 32K for 270M/1B)"],"output_types":["text (streaming or buffered)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.across.140.languages","name":"multilingual text generation across 140+ languages","description":"Gemma 3 is trained on data spanning 140+ languages, enabling text generation, summarization, and question-answering in non-English languages without language-specific fine-tuning. Language selection is implicit from input text; no explicit language parameter is required. Quality and coverage vary by language based on training data distribution, which is not publicly documented.","intents":["Build chatbots and content generation systems for global audiences","Implement machine translation-like capabilities without dedicated translation models","Create multilingual customer support agents","Generate summaries and Q&A in languages beyond English"],"best_for":["Teams building products for non-English markets without language-specific models","Developers prototyping multilingual systems before investing in specialized models","Builders supporting low-resource languages where dedicated models are unavailable"],"limitations":["Training data composition and language distribution not disclosed; some languages likely undertrained","No published benchmarks on multilingual performance (e.g., FLORES, XQuAD scores)","Quality likely degrades for low-resource or morphologically complex languages","No explicit language tagging or language-specific prompting guidance documented"],"requires":["Ollama 0.6 or later","Any Gemma 3 variant (270M to 27B)"],"input_types":["text in any of 140+ supported languages"],"output_types":["text in the same language as input"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_4","uri":"capability://tool.use.integration.local.rest.api.inference.via.ollama","name":"local rest api inference via ollama","description":"Gemma 3 models are served locally via Ollama's REST API (http://localhost:11434/api/chat), supporting chat completion format with streaming responses. The API abstracts model loading, GPU memory management, and inference scheduling, allowing developers to integrate Gemma 3 without direct CUDA/GPU programming. Requests are processed sequentially or in parallel depending on GPU memory availability and Ollama's internal scheduling.","intents":["Integrate local LLM inference into existing applications without cloud dependencies","Build AI features that require sub-100ms latency or offline operation","Prototype LLM applications without API keys or cloud service costs","Stream model outputs to frontend applications in real-time"],"best_for":["Solo developers building local-first AI tools","Teams with privacy requirements preventing cloud model usage","Builders prototyping before committing to cloud inference costs"],"limitations":["Sequential request processing on single GPU; concurrent requests may queue with unpredictable latency","No built-in request authentication or rate limiting; suitable for local/trusted networks only","Streaming responses require client-side handling of chunked transfer encoding","No load balancing or failover; single Ollama process is a single point of failure"],"requires":["Ollama 0.6 or later installed and running","Model pulled locally: `ollama pull gemma3:27b`","GPU with sufficient VRAM for chosen variant"],"input_types":["JSON with chat format: {\"role\": \"user\", \"content\": \"...\"}"],"output_types":["JSON with streaming chunks: {\"message\": {\"content\": \"...\"}}"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_5","uri":"capability://tool.use.integration.python.and.javascript.sdk.integration","name":"python and javascript sdk integration","description":"Gemma 3 is accessible via Ollama's Python and JavaScript SDKs, providing language-native abstractions for chat completion, streaming, and model management. The SDKs wrap the REST API, handling serialization, streaming, and error handling. Python SDK supports async/await patterns; JavaScript SDK supports both Node.js and browser environments (via fetch).","intents":["Integrate Gemma 3 into Python data science and ML workflows","Build JavaScript/Node.js applications with local LLM inference","Use async/await patterns for non-blocking inference in Python","Prototype LLM features in Jupyter notebooks or REPL environments"],"best_for":["Python developers using pandas, scikit-learn, or Jupyter for prototyping","JavaScript/Node.js developers building full-stack AI applications","Teams with existing Python/JS codebases integrating LLM features"],"limitations":["SDKs are thin wrappers around REST API; no client-side optimization or caching","Async support in Python SDK may not fully utilize GPU if requests are not properly batched","Browser-based JavaScript SDK requires CORS-enabled Ollama instance; not suitable for production web apps","No built-in retry logic, timeout handling, or circuit breaker patterns"],"requires":["Python 3.7+ with `pip install ollama` or Node.js 14+ with `npm install ollama`","Ollama 0.6 or later running locally"],"input_types":["Python: dict or Message objects; JavaScript: object literals"],"output_types":["Python: ChatResponse objects or async generators; JavaScript: Promise<ChatResponse>"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_6","uri":"capability://tool.use.integration.cloud.hosted.inference.with.usage.based.pricing","name":"cloud-hosted inference with usage-based pricing","description":"Gemma 3 is available as cloud-hosted variants (gemma3:4b-cloud, gemma3:12b-cloud, gemma3:27b-cloud) via Ollama Cloud, with usage-based pricing tiers (Free: 1 concurrent model; Pro: $20/mo for 3 concurrent models; Max: $100/mo for 10 concurrent models). Requests are routed to Ollama-managed infrastructure; no local GPU required. Cloud models support the same REST API and SDK interfaces as local models, enabling seamless switching between local and cloud deployment.","intents":["Scale inference without managing GPU infrastructure","Use Gemma 3 on machines without GPUs (laptops, mobile backends)","Prototype with cloud models before committing to local deployment","Run multiple models concurrently without GPU memory constraints"],"best_for":["Teams without GPU infrastructure or capital for hardware","Developers building serverless or containerized applications","Builders needing elastic scaling for variable workloads"],"limitations":["Concurrency limits enforce queuing; requests exceeding tier limits are queued with unknown queue size and timeout behavior","Cloud models subject to usage limits (exact limits per tier not documented)","Latency includes network round-trip; no published latency benchmarks vs. local inference","Requires internet connectivity; not suitable for offline or air-gapped deployments","Pricing is usage-based but exact cost per token or request not specified"],"requires":["Ollama Cloud account (free tier available)","Ollama 0.6 or later with cloud authentication configured","Internet connectivity"],"input_types":["JSON with chat format (same as local API)"],"output_types":["JSON with streaming chunks (same as local API)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_7","uri":"capability://tool.use.integration.tool.calling.and.function.invocation.for.agent.workflows","name":"tool calling and function invocation for agent workflows","description":"Gemma 3 cloud models support tool calling via a schema-based function registry, enabling agents to invoke external functions (APIs, databases, tools) as part of reasoning chains. Tools are defined as JSON schemas; the model outputs structured function calls that are executed by the agent framework. This enables multi-step reasoning workflows where the model decides which tools to invoke and in what order.","intents":["Build AI agents that can call APIs, query databases, or execute code","Implement multi-step reasoning where the model decides which tools to use","Create autonomous workflows that combine LLM reasoning with external systems","Enable function calling without manual prompt engineering"],"best_for":["Developers building autonomous agents or AI assistants","Teams implementing AI-powered automation workflows","Builders creating chatbots that need to interact with external systems"],"limitations":["Tool calling only available in cloud models (gemma3:*-cloud); local variants do not support tool calling","Tool schema format and validation rules not documented","No published benchmarks on tool calling accuracy or hallucination rates","Tool execution is agent-side responsibility; no built-in tool execution framework"],"requires":["Ollama Cloud account (Pro or Max tier recommended for concurrent tool calls)","Ollama 0.6 or later with cloud authentication","Tool schemas defined as JSON (format not specified in documentation)"],"input_types":["JSON with chat format + tools array"],"output_types":["JSON with tool_calls array containing function name, arguments, and call ID"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_8","uri":"capability://text.generation.language.streaming.response.generation.with.chunked.output","name":"streaming response generation with chunked output","description":"Gemma 3 supports streaming responses via Ollama's REST API and SDKs, delivering model output in real-time chunks rather than waiting for full completion. Streaming is implemented via HTTP chunked transfer encoding; clients receive partial responses as they are generated, enabling low-latency user feedback and progressive rendering in UIs. Streaming can be disabled for batch processing or when full responses are required.","intents":["Build responsive chatbot UIs that show model output as it's generated","Implement progressive text rendering in web applications","Reduce perceived latency by streaming partial results","Process long outputs without buffering entire responses in memory"],"best_for":["Frontend developers building interactive chat interfaces","Teams implementing real-time AI features in web/mobile apps","Builders creating streaming dashboards or live output displays"],"limitations":["Streaming adds complexity to client-side handling (chunked encoding, partial JSON parsing)","No built-in backpressure handling; fast clients may overwhelm slow networks","Streaming latency depends on model inference speed; no optimization for time-to-first-token","Browser-based streaming requires CORS-enabled Ollama instance"],"requires":["Ollama 0.6 or later","Client-side streaming support (fetch API with ReadableStream, or SDK with async generators)","HTTP/1.1 or HTTP/2 support for chunked transfer encoding"],"input_types":["JSON with chat format + stream: true parameter"],"output_types":["HTTP chunked response with JSON objects per chunk"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-gemma3__cap_9","uri":"capability://automation.workflow.model.management.and.lifecycle.via.ollama.cli","name":"model management and lifecycle via ollama cli","description":"Gemma 3 models are managed via Ollama's command-line interface, supporting pull (download), run (execute), list (enumerate), and rm (delete) operations. Models are stored locally in a cache directory; pulling downloads the GGUF artifact from Ollama's registry. The CLI abstracts model versioning, GPU memory management, and process lifecycle, allowing developers to manage models without direct system administration.","intents":["Download and cache Gemma 3 models locally","Switch between model sizes without manual artifact management","Clean up disk space by removing unused models","Verify model availability and version information"],"best_for":["Developers managing multiple models on local machines","Teams automating model deployment in CI/CD pipelines","Builders prototyping with different model sizes"],"limitations":["No built-in model versioning or rollback; pulling latest overwrites previous versions","No model integrity verification (checksums, signatures) documented","Model cache location is fixed; no built-in support for custom cache directories","No model update notifications or automatic updates"],"requires":["Ollama 0.6 or later installed and in PATH","Disk space for model artifacts (292MB to 17GB depending on variant)","Internet connectivity for initial pull"],"input_types":["CLI commands: ollama pull, ollama run, ollama list, ollama rm"],"output_types":["CLI output: model metadata, download progress, error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama 0.6 or later","NVIDIA GPU with 2GB+ VRAM for 270M/1B variants; 8GB+ for 12B; 16GB+ for 27B (estimated)","Python 3.7+ or Node.js 14+ for SDK usage","Ollama 0.6 or later with vision support enabled","4B variant minimum (3.3GB disk); 12B or 27B recommended for complex visual reasoning","Image files in common formats (PNG, JPEG, WebP assumed but not explicitly stated)","12B or 27B variant recommended for complex reasoning; 4B may struggle","Sufficient context window for multi-step reasoning (128K available)","Ollama 0.6 or later with GGUF support","Disk space for GGUF artifacts (292MB to 17GB)"],"failure_modes":["Exact quality degradation from QAT vs. full-precision models is undocumented","GPU VRAM requirements per variant not specified; requires empirical testing","CPU inference possible via Ollama fallback but not officially benchmarked for Gemma 3","Inference latency/throughput metrics not published; performance varies by hardware","Vision capability only available in 4B, 12B, 27B variants; 270M and 1B are text-only","Image input format specifications (resolution, file types, max dimensions) not documented","No benchmark data on vision performance vs. specialized vision models (CLIP, LLaVA)","Image encoding adds latency and context window consumption; exact overhead unknown","Reasoning improvements are claimed but not benchmarked against baselines (no MMLU, GSM8K, HumanEval scores published)","No explicit reasoning prompting techniques documented (e.g., chain-of-thought, step-by-step)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gemma3","compare_url":"https://unfragile.ai/compare?artifact=gemma3"}},"signature":"lqjlByzR8vQmacmvyjZu5/3gn6wTrtPp7eXOg66xqB2PeIhVYPhtfwUJC3bm51iGzfMwbcoDsSa4p8wqu30SDw==","signedAt":"2026-06-21T14:47:25.057Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gemma3","artifact":"https://unfragile.ai/gemma3","verify":"https://unfragile.ai/api/v1/verify?slug=gemma3","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}