Ollama
softwareAbout
Run open-source LLMs locally on your machine. Simple CLI to download and run Llama, Mistral, Gemma, and other models with no setup.
Overview
Ollama makes running open-source LLMs locally as simple as a single command, democratizing access to models like Llama, Mistral, and Gemma for developers who want private, offline AI capabilities.
Pros
- +Run models locally
- +Dead simple
- +Privacy (no data leaves machine)
- +Free
Cons
- -Needs powerful hardware
- -Smaller models than cloud
- -No fine-tuning features
This may be an affiliate link — the creator and GuruStacks may earn a commission, at no extra cost to you. Learn more
Details
Pricing
Model
open source
Platforms
Community
Listed in Stacks
AI Model Training
Complete toolkit for training, fine-tuning, and deploying AI/ML models — from experiment tracking and GPU compute to data labeling, model serving, and vector databases.
Vibe Coding
The ultimate toolkit for vibe coding — AI-powered development tools, no-code builders, and rapid prototyping platforms that let you build by describing what you want.
Related
Similar tools
View alternatives →OpenAI API
4.4API platform for accessing GPT-4, DALL-E, Whisper, and other AI models. Build AI-powered features including text generation, image creation, speech-to-text, and embeddings.
Feast
4.4Open source feature store delivering structured data to AI and LLM applications at scale for training and inference.
vLLM
4.7High-throughput LLM inference engine with PagedAttention for efficient memory management. The fastest open-source LLM serving solution.
Ragas
4.5Open-source framework for evaluating RAG pipelines. Measures faithfulness, relevancy, context precision, and other RAG-specific metrics.
LangGraph
4.4Open-source framework for building controllable, stateful multi-agent applications by modeling agents as state graphs, part of the LangChain ecosystem with 24,000+ GitHub stars.
Scikit-learn
4.8Python machine learning library offering simple and efficient tools for classification, regression, clustering, and preprocessing built on NumPy and SciPy.