𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗢𝗻-𝗗𝗲𝘃𝗶𝗰𝗲 𝗔𝗜 𝗪𝗶𝘁𝗵 𝗢𝗹𝗹𝗮𝗺𝗮

Cloud AI models cause three main problems:

  • Network latency delays your app.
  • Token costs change constantly.
  • Data privacy risks grow.

Local inference is no longer an experiment. It is a requirement for enterprise tools.

Ollama lets you run models like Llama 3.2 or Gemma on your own hardware. Most people use the terminal. Developers should use the API.

Ollama runs an HTTP engine on localhost:11434. You can connect web microservices to this engine. This setup removes external network dependencies.

One key tool is the POST /api/generate endpoint.

Use this for stateless tasks. It works well for:

  • Generating JSON data.
  • Classifying text in the background.
  • Creating metadata.

Use this endpoint when you do not need a conversation history.

Example command:

curl http://localhost:11434/api/generate -d '{ "model": "llama3.2", "prompt": "Explain Quantum Computing in one short sentence.", "stream": false }'

Choosing the right inference pattern helps your app handle data streams.

Source: https://dev.to/nube_colectiva_nc/mastering-on-device-ai-orchestration-a-deep-dive-into-ollamas-local-api-3abk

Optional learning community: https://t.me/GyaanSetuAi