Ollama
The easiest way to run large language models locally
Run Llama 2 and other models on macOS, with Windows and Linux coming soon. Customize and create your own.
Reviews for Ollama
Hear what real users highlight about this tool.
Makers consistently praise Ollama for fast local iteration, privacy, and control. The makers of Sequoia liken it to an in-house AI lab with zero latency and no GPU bills. The makers of Portia AI call it a universal connector for local models in their SDK, while the makers of Znote highlight secure, offline use. Users echo the simplicity—easy setup, Docker-like workflows, quick prototyping, solid performance, and cost savings. Some note best results with mid-size models and smooth integrations via APIs.
This AI-generated snapshot distills top reviewer sentiments.
Made it possible to run local LLMs easily, without needing API keys, speeding up experimentation and prototyping.
Solid local AI tool. Easy setup, decent performance, saves API costs. Worth trying.
We’re exploring Ollama to test and run LLMs locally—faster iteration, zero latency, total control. It’s like having our own AI lab, minus the GPU bills
Universal connector for all the local models hooking into our SDK.
Ollama inspired me to create this app
Ollama makes it super easy for us to let user choose models and install them through our GUI system.
Local LLMs for rapid prototyping, evals, and privacy-safe experiments. Model swapping = faster iteration, fewer cloud waits.
Made local inference trivial, bundling models and serving a simple API, enabling offline development, reproducible experiments, and fast iterations daily.
It’s been a game-changer for me during the building phase of GroupVoyage. Running models locally with zero hassle and super-fast responses made it so much easier to brainstorm, test ideas, and refine features.
If you’re a maker or developer, definitely worth checking out. It feels like having an AI co-pilot right on your machine 🚀.
Ollama was the key to enabling AGINT’s local LLM execution. It allowed AGINT to run models fully on-device without sending data to the cloud, ensuring offline operation, higher privacy, and faster response times for certain workloads.
I’ve been tinkering with Ollama to spin up LLMs like Llama 3 and Qwen on my laptop, feels like having Docker for AI without the headache.
Faster setup compared to hugging face (subjective opinion btw) due to not needing to manually setup llama.cpp. Downside is that it offers scarce amount of models unlike hugging face.
Huge thanks to Ollama — the powerhouse behind local LLMs. Without their amazing work making it dead simple to run models like Mistral or Phi entirely offline, LogWhisperer wouldn’t exist. If you're building secure, local-first AI tools, Ollama is essential.