Enterprise DNA Enterprise DNA
O Open Source Frameworks medium

ollama

by Community

Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

OSS

ollama

Added 1 June 2026

#deepseek #gemma #gemma3 #glm #go #golang #gpt-oss #llama

Overview

Ollama is a Go-based framework for running large language models locally on your machine. It downloads and executes open-source models like Llama, Mistral, and others without requiring cloud infrastructure or API keys.

Best for

Best for
Developers building local-first applications or prototyping with open-source LLMs without cloud costs

Use cases

  • Running LLMs offline for privacy-sensitive applications
  • Local development and testing before deploying to production
  • Building chatbots and agents that don't depend on external APIs

Notes

Ollama is a Go-based framework for running large language models locally on your machine. It downloads and executes open-source models like Llama, Mistral, and others without requiring cloud infrastructure or API keys.

172,846 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

  • Running LLMs offline for privacy-sensitive applications
  • Local development and testing before deploying to production
  • Building chatbots and agents that don’t depend on external APIs

Pros

  • No cloud dependency or API costs once models are downloaded
  • Simple CLI interface with minimal setup overhead
  • Supports a wide range of open-source models with one-command installation

Cons

  • Requires significant local compute and storage for larger models
  • Performance depends entirely on your hardware, not optimized cloud infrastructure
  • Limited to open-source models, no access to proprietary models like GPT-4

Indexed from awesome-llm and enriched against its public facts.

Pros

  • No cloud dependency or API costs once models are downloaded
  • Simple CLI interface with minimal setup overhead
  • Supports a wide range of open-source models with one-command installation

Cons

  • Requires significant local compute and storage for larger models
  • Performance depends entirely on your hardware, not optimized cloud infrastructure
  • Limited to open-source models, no access to proprietary models like GPT-4

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