Run Chinese AI Models Locally with Ollama: Complete Guide

July 29, 2026 · 10 min read

Running AI models locally gives you privacy, zero latency, and no recurring costs. With Ollama, setting up Chinese AI models like DeepSeek, Qwen, and GLM on your own machine takes just a few minutes.

This guide walks through everything: hardware requirements, installation, pulling models, running them, and integrating with your development workflow.

Hardware Requirements

For most developers, a 7-8B model on a machine with 16GB RAM provides the best balance of quality and speed. Qwen 2.5 7B and DeepSeek-Coder-V2 7B are excellent choices in this tier.

Installation

macOS / Linux

curl -fsSL https://ollama.com/install.sh | sh

Windows

# Download from https://ollama.com/download
# Or use winget:
winget install Ollama.Ollama

Pull and Run Chinese Models

DeepSeek

# DeepSeek Coder V2 (7B) - great for coding
ollama pull deepseek-coder-v2:7b
ollama run deepseek-coder-v2:7b "Write a Python function to merge two sorted lists"

# DeepSeek R1 Distill (8B) - good reasoning
ollama pull deepseek-r1:8b
ollama run deepseek-r1:8b "Explain why the sky is blue"

Qwen

# Qwen 2.5 (7B) - best all-rounder
ollama pull qwen2.5:7b
ollama run qwen2.5:7b "用中文解释什么是机器学习"

# Qwen 2.5 (14B) - better quality
ollama pull qwen2.5:14b
ollama run qwen2.5:14b "Translate this to Japanese: Hello, how are you?"

GLM

# GLM-4 (9B)
ollama pull glm4:9b
ollama run glm4:9b "Summarize the key differences between REST and GraphQL"

Python Integration

import ollama

# Chat with a local model
response = ollama.chat(model='qwen2.5:7b', messages=[
  {'role': 'user', 'content': 'Explain quantum computing in 3 sentences.'}
])
print(response['message']['content'])

# Streaming
for chunk in ollama.chat(model='deepseek-coder-v2:7b', messages=[
  {'role': 'user', 'content': 'Write a FastAPI endpoint for user authentication'}
], stream=True):
  print(chunk['message']['content'], end='', flush=True)

OpenAI-Compatible API

Ollama exposes an OpenAI-compatible endpoint on http://localhost:11434/v1, so any tool that works with OpenAI also works with local models:

from openai import OpenAI

client = OpenAI(
    api_key="ollama",  # anything works
    base_url="http://localhost:11434/v1"
)

response = client.chat.completions.create(
    model="qwen2.5:7b",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Local vs API: When to Use Which

Frequently Asked Questions

Can I run DeepSeek locally?

Yes, you can run quantized versions of DeepSeek, Qwen, GLM, and other Chinese models locally using Ollama. A 7B parameter model needs 4-6GB RAM, while a 14B model needs 8-12GB. Performance is comparable to the API for most tasks.

Can I switch between local and API models?

Yes. Both Ollama and AIWave expose OpenAI-compatible APIs. You can build your app once and switch between http://localhost:11434/v1 and https://api.aiwave.live/v1 by just changing the base URL.

Which local model is best for Chinese text?

Qwen 2.5 7B/14B is the best local model for Chinese text. It was trained by Alibaba on massive Chinese corpora and handles both Simplified and Traditional Chinese natively. GLM-4 9B is also excellent.