Run Large AI Models Locally on Mac with OMLX - 10x Inference Speed Boost!
In the past month, more and more people have started running local AI large models on Mac. For example, using Ollama to run various models, then calling them through OpenCat or Ollama desktop clients. But many people have a very painful experience: slow speed, stuttering inference, only single-digit tokens per second.
This is especially obvious on Mac Mini or 16GB memory devices. Today, I'll introduce a Mac local model acceleration tool - OMLX.
It can boost local model inference speed by more than 10 times, even budget Mac Mini can easily run large models.
1. Why Do We Need OMLX?¶
When many people run local models on Mac, they generally use this architecture:
User Interface (OpenCat/Desktop Client) → Ollama → Local Model
But by default:
- Low inference efficiency
- Low KV Cache utilization
- Insufficient CPU/GPU scheduling
So this often happens:
- Words popping out one by one
- 3~5 tokens per second
- Simple questions taking tens of seconds or even minutes
This is a very poor experience for daily use.
2. What is OMLX?¶
OMLX is a Mac local AI model acceleration server, main features include:
- ✅ Optimize local model inference
- ✅ Boost token generation speed
- ✅ Manage model cache
- ✅ Provide OpenAI API interface
- ✅ Support stress testing
Simple understanding: OMLX = Mac Local AI Model Acceleration Server
After deployment, local model speed usually can increase by 5~10 times or more.
3. Mac Mini Recommended Model Configuration¶
If your device is 16GB Mac Mini, recommended configuration:
| Model | Size | Recommended Device |
|---|---|---|
| Qwen3.5 4B | ~3GB | 8GB Mac |
| Qwen3.5 9B | ~6.6GB | 16GB Mac |
| Qwen3.5 27B | ~17GB | 32GB+ |
9B model is very balanced between performance and quality, best choice for 16GB Mac Mini.
4. Install Ollama¶
First install Ollama:
- Open official website to download and install: https://ollama.com
- After installation, open terminal
- Download Qwen3.5 9B model:
ollama pull qwen3.5:9b
Download size: about 6.6GB
After download completes, you can test the model:
ollama run qwen3.5:9b "2,6,12,20,30,(?) What is the pattern of this sequence?"
But under Ollama default inference, speed may be very slow:
| Item | Time |
|---|---|
| Start generating | 20 seconds |
| Complete answer | 1 min 50 sec |
5. Install OMLX¶
5.1 Prerequisites¶
Before installation, please ensure OpenClaw is installed on your current Mac. If not installed, you can use the one-click install command below:
curl -fsSL https://openclaw.ai/install.sh | bash
Currently OpenClaw has 4000+ Stars on GitHub.
5.2 Download OMLX¶
Open the project Release page to download the latest version:
- GitHub: https://github.com/jundot/omlx
- Cloud drive packaged download: https://pan.quark.cn/s/b9503bb71e13
Note to choose correct version:
| File Version | Suitable Device |
|---|---|
| square version | Old Mac |
| tar version | M5 / Latest macOS |
After download, drag directly into Applications to install.
6. Start OMLX Server¶
After opening OMLX, configure as follows:
- Default port: 8000
- API Key: Set anything, for example:
12345678
Click start, when you see green status it means startup successful.
Enter the backend management interface for further configuration.
7. Configure Model Cache (Very Critical)¶
In settings, recommend configuring like this:
Memory Limits¶
If it's 16GB Mac, suggest setting:
- Hot cache: 4GB
- Cold cache: 8GB
Cold Cache (Strongly Recommended)¶
Function:
- Save KV cache
- Model starts faster next time
- Greatly improve context inference efficiency
8. Download Models¶
Note: OMLX doesn't recognize Ollama model format, so need to re-download models.
In OMLX backend:
- Search model:
qwen3.5:9b - Download directly
- After download completes, auto-loads
9. Connect to OpenCat¶
Next connect OMLX to OpenCat:
- Run OpenCat in terminal
- Configure Provider as
Custom Provider - API address:
http://localhost:8000/v1 - API Key: Leave empty (or fill in your set key)
- Model ID: Copy the model ID from OMLX backend
After configuration, can use.
10. Speed Test Comparison¶
Same question: 2,6,12,20,30,(?) What is the pattern of this sequence?
| Solution | Time Used |
|---|---|
| Ollama Native | 1 min 50 sec |
| OMLX Acceleration | 10~15 seconds |
Speed boost close to 10 times! Almost achieves second-level response.
11. OMLX Advanced Features¶
1. Performance Matrix Testing¶
Can test:
- Single-thread performance
- Multi-thread performance
- Concurrent stress
Used to evaluate model performance under different loads.
2. OpenAI API Compatible¶
Supports:
- OpenAI API format
- Cloud model access
- Custom model configuration
Can directly use as local OpenAI server.
3. KV Cache Persistence¶
Greatly improve:
- Model startup speed
- Context inference efficiency
- Multi-turn conversation experience
12. Summary Recommendation¶
If you want to run AI large models locally on Mac, this combination is highly recommended:
OMLX + Ollama + OpenCat
Advantages:
- ✅ Local operation, privacy and security
- ✅ No token consumption, free to use
- ✅ Inference speed greatly improved (5-10 times)
- ✅ Mac Mini can also run easily
- ✅ Support multiple model free switching
Especially for friends who like to tinker with local AI + automation tools, this solution is really very good.
Related Resources:
- OMLX GitHub: https://github.com/jundot/omlx
- OpenClaw: https://openclaw.ai
- Qwen3.5 Model: https://ollama.com/library/qwen3.5
Hope this blog post is helpful to you!