Qwen Qwen3 5

Qwen 3.5 by Alibaba — run Qwen 3.5 (the latest and most capable Qwen model) across your local device fleet. Qwen 3.5 rivals GPT-4o and Claude 3.5 on reasoning benchmarks. Plus Qwen3-Coder for code generation and Qwen3-ASR for speech-to-text. Fleet-routed to the best available machine via Ollama Herd. Zero cloud costs.

Qwen 3.5 — Alibaba's Latest LLM on Your Local Fleet

Qwen 3.5 is the newest and most capable model in the Qwen family. It rivals GPT-4o and Claude 3.5 Sonnet on reasoning, coding, and multilingual benchmarks — and you can run it locally on your own hardware for free.

Supported Qwen models

Model	Parameters	Ollama name	Best for
Qwen 3.5	72B	qwen3.5	Frontier reasoning — rivals GPT-4o
Qwen 3.5	32B	qwen3.5:32b	Strong quality at lower resource cost
Qwen 3.5	14B	qwen3.5:14b	Good balance for mid-range hardware
Qwen 3.5	7B	qwen3.5:7b	Fast on low-RAM devices
Qwen3-Coder	32B	qwen3-coder:32b	Code generation — 80+ languages
Qwen2.5-Coder	7B, 32B	qwen2.5-coder:32b	Proven code model
Qwen3-ASR	—	qwen3-asr	Speech-to-text transcription

Quick start

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. Models are pulled on demand. All pulls require user confirmation.

Use Qwen 3.5 through the fleet

OpenAI SDK

from openai import OpenAI

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

# Qwen 3.5 for complex reasoning
response = client.chat.completions.create(
    model="qwen3.5",
    messages=[{"role": "user", "content": "Compare microservices vs monolith architectures"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Qwen3-Coder for code

response = client.chat.completions.create(
    model="qwen3-coder:32b",
    messages=[{"role": "user", "content": "Write a thread-safe connection pool in Go"}],
)
print(response.choices[0].message.content)

Ollama API

# Qwen 3.5 chat
curl http://localhost:11435/api/chat -d '{
  "model": "qwen3.5",
  "messages": [{"role": "user", "content": "Explain attention mechanisms"}],
  "stream": false
}'

Qwen3-ASR speech-to-text

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

Hardware recommendations

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

Device	RAM	Best Qwen model
Mac Mini (16GB)	16GB	qwen3.5:7b
Mac Mini (32GB)	32GB	qwen3.5:14b or qwen2.5-coder:32b
MacBook Pro (64GB)	64GB	qwen3.5:32b or qwen3-coder:32b
Mac Studio (128GB)	128GB	qwen3.5 (72B) — full quality
Mac Studio (256GB)	256GB	qwen3.5 + qwen3-coder:32b simultaneously

Why Qwen 3.5 locally

• GPT-4o quality — Qwen 3.5 72B matches GPT-4o on MMLU, HumanEval, and MT-Bench
• Zero cost — no per-token charges after hardware
• Privacy — all data stays on your network
• No rate limits — Qwen's cloud API throttles during peak hours. Your hardware doesn't.
• Fleet routing — multiple machines share the load

Also available on this fleet

Other LLMs

Llama 3.3, DeepSeek-V3, DeepSeek-R1, Phi 4, Mistral, Gemma 3, Codestral — same endpoint.

Image generation

curl -o image.png http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "an AI assistant helping with code", "width": 1024, "height": 1024}'

Embeddings

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Qwen 3.5 large language model"}'

Monitor

curl -s http://localhost:11435/fleet/status | python3 -m json.tool
curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

Dashboard at http://localhost:11435/dashboard.

Full documentation

• Agent Setup Guide
• API Reference

Contribute

Ollama Herd is open source (MIT):

• Star on GitHub — help others run Qwen locally
• Open an issue — share your Qwen setup, report bugs
• PRs welcome — CLAUDE.md gives AI agents full context. 444 tests, async Python.

Guardrails

• Model downloads require explicit user confirmation — Qwen models range from 4GB (7B) to 42GB (72B).
• 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.