Ollama on Edge

Model recommendations, auto-start, and pull helpers for running Ollama on Raspberry Pi.

Ollama Edge Helpers

Utilities for running Ollama on resource-constrained edge devices — ensure it’s running, pull models, and get hardware-appropriate recommendations.

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Quick Start

import { ensureOllama, pullModel, recommendModel } from "@radaros/edge";
import * as os from "node:os";

// Ensure Ollama is running (starts it if needed)
const status = await ensureOllama();
console.log(status);
// { running: true, version: "0.1.0", models: ["tinyllama:1.1b"] }

// Get the best model for available RAM
const ramMb = os.totalmem() / 1024 / 1024;
const rec = recommendModel(ramMb);
console.log(rec);
// { model: "phi3:mini", label: "capable", parameterSize: "3.8B", ramRequired: 4000 }

// Pull the model if not cached
await pullModel(rec.model);

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Functions

ensureOllama(baseUrl?)

Check if Ollama is running. If not, spawn ollama serve and wait up to 10 seconds for it to become available.

const status = await ensureOllama();
// { running: boolean, version?: string, models?: string[], error?: string }

checkOllama(baseUrl?)

Read-only check — does not attempt to start Ollama.

pullModel(model, opts?)

Pull a model from the Ollama registry if not cached locally.

const result = await pullModel("tinyllama:1.1b");
// { success: true, model: "tinyllama:1.1b" }

recommendModel(ramMb)

Pick the largest model that fits comfortably in available RAM (with 30% headroom for OS and Node.js).

RAM	Recommended Model	Label
1 GB	tinyllama:1.1b	fast
2 GB	tinyllama:1.1b	fast
3 GB	llama3.2:1b	balanced
6 GB	phi3:mini	capable
8+ GB	mistral:7b	powerful

hasModel(model, baseUrl?)

Check if a specific model is cached locally.

listModelTiers()

Get all known model tiers with their RAM requirements.

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Installing Ollama on Raspberry Pi

curl -fsSL https://ollama.com/install.sh | sh
ollama pull tinyllama:1.1b

For Pi 4 with 2 GB RAM, tinyllama:1.1b is the only viable option. Pi 5 with 8 GB can comfortably run phi3:mini (3.8B parameters).