{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ollama","slug":"ollama","name":"Ollama","type":"cli","url":"https://github.com/ollama/ollama","page_url":"https://unfragile.ai/ollama","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ollama__cap_0","uri":"capability://text.generation.language.local.llm.model.execution.with.ggml.inference","name":"local-llm-model-execution-with-ggml-inference","description":"Executes large language models entirely on local hardware using GGML (Generative Graph Modeling Language) quantized format, which enables CPU and GPU inference without cloud dependencies. Ollama packages pre-quantized models (Q4, Q5, Q8 variants) and handles memory-efficient loading through mmap-based file access, allowing models up to 70B parameters to run on consumer hardware with 8-16GB RAM.","intents":["Run LLMs locally without sending data to cloud providers","Reduce latency for real-time inference in production systems","Develop and test LLM applications offline or in air-gapped environments","Reduce per-inference costs by eliminating API call fees"],"best_for":["developers building privacy-critical LLM applications","teams with strict data residency requirements","researchers prototyping LLM behavior without cloud costs","edge deployment scenarios requiring offline inference"],"limitations":["Inference speed 5-10x slower than cloud APIs (GPT-4) on CPU-only systems","Requires 8GB+ RAM for 7B models; 16GB+ for 13B models; 32GB+ for 70B models","GPU acceleration limited to NVIDIA CUDA, AMD ROCm, and Apple Metal — no Intel Arc or Qualcomm support","Model quantization reduces output quality compared to full-precision versions","No built-in distributed inference across multiple machines"],"requires":["macOS 11+, Linux (Ubuntu 20.04+, Fedora, Debian), or Windows 10+ with WSL2","4GB minimum RAM (8GB recommended for 7B models)","NVIDIA GPU with CUDA Compute Capability 5.0+ (optional, for acceleration)","~4-40GB disk space depending on model size"],"input_types":["text prompts","multi-turn conversation history","system prompts and role definitions"],"output_types":["text completions","streaming token output","structured JSON (via prompt engineering)"],"categories":["text-generation-language","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_1","uri":"capability://automation.workflow.model.library.management.with.registry.pull","name":"model-library-management-with-registry-pull","description":"Provides a centralized model registry (ollama.ai/library) with one-command model downloading, versioning, and caching. Models are pulled via `ollama pull <model>` which fetches pre-quantized GGML binaries in layers (similar to Docker), deduplicates identical weights across model variants, and stores them in ~/.ollama/models with automatic cleanup of unused versions.","intents":["Quickly switch between different LLM models without manual downloading","Manage multiple model versions and sizes (7B, 13B, 70B) in one place","Share model configurations and weights across team members via registry","Reduce disk usage by deduplicating shared weights between model variants"],"best_for":["teams evaluating multiple LLM models for a use case","developers prototyping with different model architectures","organizations standardizing on specific model versions"],"limitations":["Registry is centralized (ollama.ai) — no built-in support for private/self-hosted registries","Model versioning uses semantic tags but no explicit dependency pinning mechanism","Pulling large models (70B) requires 40GB+ free disk space and 30+ minutes on typical internet","No built-in model fine-tuning or custom quantization — requires external tools like llama.cpp"],"requires":["Ollama CLI installed and running as daemon","Internet connection for initial model pull","Sufficient disk space (4GB for 7B models, 40GB for 70B models)"],"input_types":["model name and tag (e.g., 'llama2:7b', 'mistral:latest')","custom Modelfile for model configuration"],"output_types":["downloaded model binaries in GGML format","model metadata and configuration"],"categories":["automation-workflow","package-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_10","uri":"capability://automation.workflow.cross.platform.daemon.service.with.auto.startup","name":"cross-platform-daemon-service-with-auto-startup","description":"Runs Ollama as a background daemon service (via `ollama serve`) on macOS, Linux, and Windows, with optional auto-startup on system boot. The daemon manages model lifecycle, GPU memory, and concurrent requests, exposing a unified REST API endpoint (localhost:11434) for all inference operations. On macOS and Linux, it can be installed as a system service for automatic startup.","intents":["Run Ollama continuously in the background without manual startup","Ensure Ollama is available immediately after system boot","Manage Ollama as a system service with standard start/stop/restart commands","Support multiple applications accessing the same Ollama daemon"],"best_for":["developers running Ollama on personal machines for development","teams deploying Ollama on servers or edge devices","organizations requiring always-on LLM inference infrastructure"],"limitations":["Daemon runs as single process — no built-in clustering or high-availability setup","Auto-startup requires system-level permissions (sudo) — may not work in restricted environments","No built-in monitoring or health checks — requires external tools (systemd, Docker) for production reliability","Windows support via WSL2 adds complexity compared to native Linux/macOS"],"requires":["Ollama installed via official installer or package manager","System permissions to install/manage services (macOS: launchd, Linux: systemd, Windows: WSL2)","Sufficient disk space for models"],"input_types":["daemon startup commands (`ollama serve`)","service management commands (systemctl, launchctl)"],"output_types":["running daemon process","REST API endpoint availability","service status"],"categories":["automation-workflow","service-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_11","uri":"capability://data.processing.analysis.model.format.conversion.and.quantization.support","name":"model-format-conversion-and-quantization-support","description":"Supports multiple model formats (GGML, GGUF, SafeTensors) and quantization levels (Q4_0, Q4_1, Q5_0, Q8_0) through Modelfile directives, enabling users to convert and quantize models from HuggingFace or other sources into Ollama-compatible format. The system uses llama.cpp's quantization algorithms to reduce model size by 75-90% while maintaining acceptable quality, making large models runnable on consumer hardware.","intents":["Convert HuggingFace models to GGML format for local inference","Quantize full-precision models to reduce memory requirements","Choose quantization level based on quality/speed tradeoff","Create custom model variants with different quantization levels"],"best_for":["researchers experimenting with different quantization strategies","teams wanting to use custom or proprietary models with Ollama","developers optimizing model size for specific hardware constraints"],"limitations":["Quantization is lossy — Q4 models show noticeable quality degradation vs. full precision, especially for reasoning tasks","Conversion from HuggingFace requires external tools (llama.cpp, ctransformers) — not built into Ollama","No support for modern quantization methods (GPTQ, AWQ) — only basic uniform quantization","Quantization process is CPU-intensive and slow (hours for 70B models)"],"requires":["Source model in HuggingFace format or GGML/GGUF format","llama.cpp or similar conversion tool installed separately","Significant disk space for intermediate files during conversion"],"input_types":["HuggingFace model files or GGML/GGUF format","quantization level specification (Q4, Q5, Q8)"],"output_types":["quantized GGML model files","Modelfile with quantization configuration"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_2","uri":"capability://tool.use.integration.rest.api.server.for.llm.inference","name":"rest-api-server-for-llm-inference","description":"Exposes a local HTTP REST API (default port 11434) compatible with OpenAI Chat Completions API format, enabling drop-in replacement of cloud LLM APIs in existing applications. The server implements streaming responses via Server-Sent Events (SSE), batch processing, and model context window management with automatic token counting via tiktoken-compatible algorithms.","intents":["Replace OpenAI API calls with local inference without changing application code","Build LLM applications that work offline or in restricted network environments","Integrate local LLMs into existing Python/Node.js/Go applications via standard HTTP","Monitor and control inference load across multiple concurrent requests"],"best_for":["developers migrating from cloud LLM APIs to local inference","teams building LLM applications with OpenAI SDK compatibility requirement","enterprises with strict data governance requiring local processing"],"limitations":["API compatibility is partial — streaming format matches OpenAI but some parameters (e.g., logit_bias, function_calling) are not supported","No built-in authentication or rate limiting — requires reverse proxy (nginx) for production security","Concurrent request handling limited by available GPU/CPU memory — no request queuing or priority scheduling","Context window fixed per model — no dynamic context compression or sliding window implementation"],"requires":["Ollama daemon running (`ollama serve`)","HTTP client library (curl, requests, axios, etc.)","Model already pulled via `ollama pull`"],"input_types":["JSON POST body with messages array, model name, temperature, top_p, etc.","streaming request flag for SSE responses"],"output_types":["JSON response with completion text and token counts","Server-Sent Events stream for token-by-token output"],"categories":["tool-use-integration","api-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_3","uri":"capability://automation.workflow.multi.model.concurrent.serving.with.memory.management","name":"multi-model-concurrent-serving-with-memory-management","description":"Manages loading and unloading of multiple models in GPU/CPU memory based on inference requests, implementing an LRU (Least Recently Used) cache that keeps hot models in VRAM and swaps cold models to disk. The system tracks per-model memory requirements and automatically offloads models when new requests arrive for different models, preventing out-of-memory crashes while maintaining fast switching between frequently-used models.","intents":["Run multiple different LLMs on the same hardware without manual memory management","Switch between specialized models (e.g., coding model, chat model, embedding model) without restarting","Optimize GPU memory utilization by keeping only active models loaded","Support multi-model inference pipelines where different tasks use different models"],"best_for":["teams using multiple specialized LLMs for different tasks","applications requiring model switching based on user input or task type","resource-constrained environments (laptops, edge devices) with limited VRAM"],"limitations":["Model switching incurs 1-5 second latency for unload/load cycle depending on model size","LRU eviction policy is fixed — no configurable priority or affinity for specific models","No explicit memory reservation mechanism — models compete for available VRAM without guarantees","Concurrent inference on multiple models not supported — only sequential model switching"],"requires":["Ollama daemon with GPU support (NVIDIA CUDA, AMD ROCm, or Apple Metal)","Sufficient total disk space for all models (not just VRAM)","Models pre-pulled via `ollama pull`"],"input_types":["inference requests specifying different model names","implicit model switching based on request routing"],"output_types":["inference results from requested model","model load/unload status (implicit)"],"categories":["automation-workflow","resource-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_4","uri":"capability://automation.workflow.modelfile.based.model.customization.and.packaging","name":"modelfile-based-model-customization-and-packaging","description":"Allows users to create custom model variants via Modelfile (similar to Dockerfile), specifying base model, system prompts, temperature, context window, and custom parameters. The Modelfile is compiled into a distributable model artifact that can be pushed to the registry or shared locally, enabling reproducible model configurations without manual prompt engineering in application code.","intents":["Create specialized model variants with custom system prompts (e.g., coding assistant, customer support bot)","Package model configurations with specific hyperparameters for reproducibility","Share model customizations across team members via registry","Avoid hardcoding prompts in application code by baking them into the model"],"best_for":["teams building multiple specialized LLM applications","organizations standardizing model behavior across products","developers wanting to version control model configurations"],"limitations":["Modelfile syntax is custom and not compatible with other frameworks (no HuggingFace Model Card equivalent)","No support for model merging or LoRA fine-tuning — only prompt/parameter customization","Custom parameters are limited to inference settings (temperature, top_p, etc.) — no architectural modifications","No built-in testing or validation framework for Modelfile configurations"],"requires":["Ollama CLI installed","Base model already pulled","Text editor for Modelfile creation"],"input_types":["Modelfile text with FROM, SYSTEM, PARAMETER directives","base model name and optional custom parameters"],"output_types":["compiled model artifact in GGML format","model metadata and configuration"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_5","uri":"capability://data.processing.analysis.embedding.generation.for.semantic.search","name":"embedding-generation-for-semantic-search","description":"Generates dense vector embeddings from text using local embedding models (e.g., nomic-embed-text, all-minilm), enabling semantic search and RAG applications without cloud API calls. Embeddings are computed via the same REST API as text generation, supporting batch embedding of documents and returning fixed-dimension vectors (384-1024 dims depending on model) compatible with vector databases like Pinecone, Weaviate, or Milvus.","intents":["Build semantic search systems that work offline without OpenAI Embeddings API","Create RAG pipelines with local embedding models for document retrieval","Generate embeddings for similarity-based recommendation systems","Reduce embedding API costs by running models locally"],"best_for":["teams building RAG applications with privacy requirements","developers prototyping semantic search without cloud dependencies","organizations processing large document volumes where embedding costs are significant"],"limitations":["Embedding quality varies by model — smaller models (all-minilm) have lower semantic accuracy than OpenAI's text-embedding-3-large","Batch embedding API not optimized for large-scale corpus processing — no built-in batching or async processing","No built-in vector database integration — requires separate tool (Pinecone, Weaviate, etc.) for storage and retrieval","Embedding dimension fixed per model — no dynamic dimension reduction or projection"],"requires":["Embedding model pulled via `ollama pull` (e.g., `ollama pull nomic-embed-text`)","Ollama daemon running","Vector database or in-memory vector store for storing embeddings"],"input_types":["text strings or documents","batch of texts for embedding"],"output_types":["dense float vectors (384-1024 dimensions)","embedding metadata (model name, dimension)"],"categories":["data-processing-analysis","semantic-search"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_6","uri":"capability://text.generation.language.streaming.token.output.with.server.sent.events","name":"streaming-token-output-with-server-sent-events","description":"Implements Server-Sent Events (SSE) streaming for real-time token-by-token output, allowing applications to display LLM responses as they are generated rather than waiting for full completion. The streaming endpoint returns newline-delimited JSON events with partial tokens, enabling low-latency UI updates and early stopping based on user input.","intents":["Build responsive chat UIs that show LLM output in real-time","Implement early stopping when user interrupts generation","Create streaming applications that feel interactive and responsive","Monitor token generation in real-time for debugging or analytics"],"best_for":["frontend developers building chat interfaces","teams building interactive LLM applications","applications requiring low-latency user feedback"],"limitations":["SSE requires HTTP/1.1 or HTTP/2 — not compatible with HTTP/3 QUIC protocol","No built-in backpressure handling — client must consume events at generation speed or buffer memory grows","Streaming adds ~10-50ms latency per token due to serialization and network overhead","No support for streaming structured outputs (JSON) — only raw text tokens"],"requires":["HTTP client with SSE support (fetch API, axios, requests library, etc.)","Ollama daemon running","Model already pulled"],"input_types":["JSON POST body with stream: true flag","standard inference parameters (model, messages, temperature, etc.)"],"output_types":["Server-Sent Events stream with newline-delimited JSON","each event contains partial token and metadata"],"categories":["text-generation-language","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_7","uri":"capability://data.processing.analysis.context.window.and.token.counting.management","name":"context-window-and-token-counting-management","description":"Automatically manages context window limits per model using tiktoken-compatible token counting algorithms, preventing context overflow errors by truncating or summarizing input when necessary. The system tracks token usage across multi-turn conversations and provides token count estimates before inference, enabling applications to implement sliding window or summarization strategies.","intents":["Build multi-turn conversation systems that respect model context limits","Estimate token usage and costs before running inference","Implement automatic context truncation or summarization for long conversations","Debug token counting discrepancies between local and cloud LLM APIs"],"best_for":["developers building conversational AI with long chat histories","teams implementing RAG systems with large document contexts","applications requiring token usage tracking for cost estimation"],"limitations":["Token counting is approximate — actual token count may differ by 1-5% from reported count due to tokenizer variations","No built-in context compression or summarization — application must implement strategies","Context window is fixed per model — no dynamic window expansion or adaptive context management","No support for token-level granularity in streaming output — only full token counts"],"requires":["Ollama daemon running","Model with known context window size"],"input_types":["text messages or documents","conversation history as array of messages"],"output_types":["token count estimates","context window utilization percentage","truncated or summarized context if needed"],"categories":["data-processing-analysis","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_8","uri":"capability://automation.workflow.gpu.acceleration.with.multi.backend.support","name":"gpu-acceleration-with-multi-backend-support","description":"Automatically detects and utilizes available GPU hardware (NVIDIA CUDA, AMD ROCm, Apple Metal) for accelerated inference, with fallback to CPU if no GPU is available. The system handles GPU memory management, kernel compilation, and backend-specific optimizations without requiring user configuration, supporting mixed precision (FP16, INT8) for faster inference on compatible hardware.","intents":["Accelerate LLM inference 5-10x on consumer GPUs without manual CUDA setup","Run inference on Apple Silicon (M1/M2/M3) with native Metal acceleration","Support AMD GPUs via ROCm without NVIDIA-specific dependencies","Automatically fall back to CPU inference if GPU is unavailable"],"best_for":["developers on macOS with Apple Silicon wanting GPU acceleration","teams with NVIDIA GPUs wanting plug-and-play acceleration","organizations with AMD GPUs seeking open-source acceleration"],"limitations":["NVIDIA support requires CUDA Compute Capability 5.0+ (GTX 750 Ti or newer) — older GPUs fall back to CPU","AMD ROCm support is experimental and less optimized than NVIDIA CUDA","Apple Metal acceleration limited to macOS 12+ with M1/M2/M3 chips — no Intel GPU support","Mixed precision (FP16) may reduce output quality compared to FP32, requiring testing per model"],"requires":["NVIDIA: CUDA Toolkit 11.8+ and cuDNN 8.0+ (auto-installed by Ollama on first run)","AMD: ROCm 5.0+ installed separately","Apple: macOS 12+ with M1/M2/M3 chip (no additional setup required)","Sufficient VRAM: 2GB for 7B models, 6GB for 13B models, 16GB+ for 70B models"],"input_types":["inference requests (no special input format required)","implicit GPU detection based on available hardware"],"output_types":["accelerated inference results","GPU utilization metrics (implicit)"],"categories":["automation-workflow","hardware-acceleration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ollama__cap_9","uri":"capability://text.generation.language.cli.based.model.interaction.and.scripting","name":"cli-based-model-interaction-and-scripting","description":"Provides a command-line interface for interactive chat, one-off inference, and scripting via `ollama run <model>` and `ollama generate` commands. The CLI supports piping input/output for integration with shell scripts and Unix pipelines, enabling LLM inference in bash workflows without requiring HTTP API calls or application code.","intents":["Run quick LLM queries from the command line without writing code","Integrate LLM inference into bash scripts and Unix pipelines","Automate batch processing of text through LLMs","Debug model behavior interactively before integrating into applications"],"best_for":["developers and data scientists working in terminal environments","DevOps engineers automating LLM tasks in shell scripts","teams prototyping LLM workflows before building applications"],"limitations":["CLI interface is synchronous — no built-in support for concurrent requests or background jobs","No interactive editing or multi-line input handling — requires workarounds for complex prompts","Output formatting is text-only — no structured output (JSON) without post-processing","No built-in history or session management — each invocation is stateless"],"requires":["Ollama CLI installed and in PATH","Ollama daemon running (`ollama serve`)","Model already pulled via `ollama pull`"],"input_types":["command-line arguments with prompt text","piped input from stdin","file input via shell redirection"],"output_types":["text output to stdout","streaming token output in interactive mode","exit codes for scripting"],"categories":["text-generation-language","cli-tool"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["macOS 11+, Linux (Ubuntu 20.04+, Fedora, Debian), or Windows 10+ with WSL2","4GB minimum RAM (8GB recommended for 7B models)","NVIDIA GPU with CUDA Compute Capability 5.0+ (optional, for acceleration)","~4-40GB disk space depending on model size","Ollama CLI installed and running as daemon","Internet connection for initial model pull","Sufficient disk space (4GB for 7B models, 40GB for 70B models)","Ollama installed via official installer or package manager","System permissions to install/manage services (macOS: launchd, Linux: systemd, Windows: WSL2)","Sufficient disk space for models"],"failure_modes":["Inference speed 5-10x slower than cloud APIs (GPT-4) on CPU-only systems","Requires 8GB+ RAM for 7B models; 16GB+ for 13B models; 32GB+ for 70B models","GPU acceleration limited to NVIDIA CUDA, AMD ROCm, and Apple Metal — no Intel Arc or Qualcomm support","Model quantization reduces output quality compared to full-precision versions","No built-in distributed inference across multiple machines","Registry is centralized (ollama.ai) — no built-in support for private/self-hosted registries","Model versioning uses semantic tags but no explicit dependency pinning mechanism","Pulling large models (70B) requires 40GB+ free disk space and 30+ minutes on typical internet","No built-in model fine-tuning or custom quantization — requires external tools like llama.cpp","Daemon runs as single process — no built-in clustering or high-availability setup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=ollama","compare_url":"https://unfragile.ai/compare?artifact=ollama"}},"signature":"ikHn6jSjG7MIPxs0NRs9QzNEscd5lYVllEcWWBlWrWwDFND6s8z4ct5XuDUQTqGo3n+P9/zRrzeXMukbZLBFAQ==","signedAt":"2026-06-21T15:44:12.018Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ollama","artifact":"https://unfragile.ai/ollama","verify":"https://unfragile.ai/api/v1/verify?slug=ollama","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"}}