Phi Phi4

Phi 4 by Microsoft — small but powerful LLMs that run on minimal hardware. Phi-4 (14B), Phi-4-mini (3.8B), and Phi-3.5 across your device fleet. Perfect for low-RAM devices on any platform. State-of-the-art reasoning in a tiny footprint. Zero cloud costs.

Phi 4 — Microsoft's Small Models, Big Results

Phi models prove you don't need 70B parameters for great results. Phi-4 matches much larger models on reasoning benchmarks while running on hardware as modest as an 8GB MacBook Air. Route them across your fleet for even better throughput.

Supported Phi models

Model	Parameters	Ollama name	RAM needed	Best for
Phi-4	14B	phi4	10GB	Reasoning, math, code — punches way above its weight
Phi-4-mini	3.8B	phi4-mini	4GB	Ultra-fast on any device, even 8GB Macs
Phi-3.5-mini	3.8B	phi3.5	4GB	Proven lightweight model
Phi-3-medium	14B	phi3:14b	10GB	Balanced quality and speed

Quick start

bash

pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/
herd                       # start the router (port 11435)
herd-node                  # run on each device — finds the router automatically

No models are downloaded during installation. All pulls require user confirmation.

Why Phi for small devices

A Mac Mini with 16GB RAM can run Phi-4 (14B) with room to spare. A MacBook Air with 8GB runs Phi-4-mini comfortably. These models start in seconds and respond fast — ideal for devices that can't load a 70B model.

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")

# Phi-4 for reasoning
response = client.chat.completions.create(
    model="phi4",
    messages=[{"role": "user", "content": "Solve: if 3x + 7 = 22, what is x?"}],
)
print(response.choices[0].message.content)

Phi-4-mini — fastest response times

bash

curl http://localhost:11435/api/chat -d '{
  "model": "phi4-mini",
  "messages": [{"role": "user", "content": "Summarize this in 3 bullet points: ..."}],
  "stream": false
}'

OpenAI-compatible API

bash

curl http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "phi4", "messages": [{"role": "user", "content": "Write a unit test for a login function"}]}'

Ideal hardware pairings

Cross-platform: These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms.

Your device	RAM	Best Phi model	Why
MacBook Air (8GB)	8GB	phi4-mini	Fits with room for other apps
Mac Mini (16GB)	16GB	phi4	Full Phi-4 with headroom
Mac Mini (24GB)	24GB	phi4	Can run Phi-4 + an embedding model simultaneously
MacBook Pro (36GB)	36GB	phi4 + phi4-mini	Both loaded, router picks based on task

Monitor your fleet

bash

# What's loaded and where
curl -s http://localhost:11435/api/ps | python3 -m json.tool

# Fleet health overview
curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

# Model recommendations based on your hardware
curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool

Web dashboard at http://localhost:11435/dashboard — live view of nodes, queues, and performance.

Also available on this fleet

Larger LLMs (when you need more power)

Llama 3.3 (70B), Qwen 3.5, DeepSeek-R1, Mistral Large — route to a bigger machine in the fleet.

Image generation

bash

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "minimalist circuit board art", "width": 512, "height": 512}'

Speech-to-text

bash

curl http://localhost:11435/api/transcribe -F "file=@meeting.wav" -F "model=qwen3-asr"

Embeddings

bash

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Microsoft Phi small language model"}'

Full documentation

• Agent Setup Guide — all 4 model types
• API Reference — complete endpoint docs

Guardrails

• Model downloads require explicit user confirmation — Phi models are small (2-8GB) but still require confirmation.
• Model deletion requires explicit user confirmation.
• Never delete or modify files in ~/.fleet-manager/.
• No models are downloaded automatically — all pulls are user-initiated or require opt-in.