# Ollama

Run AI models locally — the open-source inference engine with 176K GitHub stars

**Verdict: recommended.** The default choice for local LLM inference — and for good reason. 176K GitHub stars, 9M+ users, MIT license, one-command setup. Runs on any hardware from Raspberry Pi 5 to dual H100s, with a model catalog spanning 4,500+ open-weight options. The OpenAI-compatible API with streaming, tool calling, structured outputs, and embeddings means no vendor lock-in — swap from Ollama to OpenAI (or vice versa) by changing one URL. Ollama Cloud (Pro $20/mo, Max $100/mo) extends the same surface to managed inference. The recent $65M raise confirms sustained investment. For any developer who wants to run AI locally — whether for privacy, cost control, or offline use — Ollama is where you start. Last verified 2026-07-10.

Ollama is the de-facto standard for running open-weight large language models on local hardware. With 176K+ GitHub stars, ~17K forks, and 9M+ users, it provides a one-command model pull/run experience (ollama pull llama3.3:8b) with an OpenAI-compatible HTTP API. It supports 4,500+ models from Llama, Qwen, Mistral, Gemma, DeepSeek, Phi, and others, with automatic quantization selection, GPU memory management, and concurrent model serving. Ollama ships native vision model support (Qwen-VL, Llama-3 vision), OpenAI-compatible structured outputs with JSON schema validation, and tool-calling parity with the Chat Completions API. The built-in Modelfile system enables custom model packaging and sharing. For production teams needing managed inference, Ollama Cloud offers Free/Pro ($20/mo)/Max ($100/mo) tiers with the same API surface as local. Backed by a benchmark-validated $65M raise, Ollama powers the local-first AI stack for privacy-sensitive, offline, and cost-optimized workloads.

## Strengths

- Local-first AI development — run 4,500+ models on your own hardware with zero API costs
- Privacy-sensitive workloads — all inference stays on your infrastructure, never leaves the machine
- Cost-controlled AI — fixed hardware cost replaces per-token billing; break-even at ~$200/mo API spend
- Offline/air-gapped environments — no internet required after model pull
- Developer toolchain integration — OpenAI-compatible API works with LangChain, LlamaIndex, Hermes Agent, Continue.dev
- Frontier model access — can only run open-weight models; no GPT-5.6, Claude, or Gemini via Ollama
- Teams without GPU hardware — running 70B+ models on CPU is impractical (<1 tok/sec)
- Zero-ops managed serving — Cloud plans exist but the local product requires hardware management

## Pricing

- Model: open-source
- Local (Free): Free
- Free: $0
- Pro: $20/mo or $200/yr billed annually
- Max: $100/mo
- Team: $500/mo (early access)
- Enterprise: Custom

Website: https://ollama.com

## Sources

- [Thunder Compute — What is Ollama: Run AI Models Locally (July 2026)](https://www.thundercompute.com/blog/what-is-ollama-run-ai-models-locally)
- [Ollama — GitHub repository](https://github.com/ollama/ollama)
- [Ollama — official website](https://ollama.com)
- [Ollama pricing page](https://ollama.com/pricing)
- [Kunal Ganglani — Best Local LLMs in 2026: Models, Hardware & Setup Guide](https://www.kunalganglani.com/blog/local-llms-complete-guide)
- [TechCrunch — Ollama raises $65M Series B, grows to nearly 9M users](https://techcrunch.com/2026/07/09/popular-open-source-ai-developer-tool-ollama-raises-65m-grows-to-nearly-9m-users/)
- [DanubeData — Run Ollama on a VPS: Self-Host Local LLMs in Europe (2026)](https://danubedata.ro/blog/run-ollama-vps-self-host-llm-2026)
- [Pooya Golchian — Ollama Cloud Pricing 2026](https://pooyagolchian.com/blog/ollama-cloud-pricing-hardware-requirements-2026/)

Canonical: https://neomanex.com/directory/ollama