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https://github.com/cyphrriot/npglue

Intel NPU Support for Goose, Zed, and much more!
https://github.com/cyphrriot/npglue

ai goose npu ollama python3 tensorflow zed

Last synced: 3 months ago
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Intel NPU Support for Goose, Zed, and much more!

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README 

          
# ɴᴘɢʟᴜᴇ - Intel NPU Ollama Replacement!



**ɴᴘɢʟᴜᴇ** provides a complete setup for running **multiple AI models** locally using OpenVINO for AI-assisted coding and development with **direct, quality answers**.



![I am a Pickle](Image/pickle_rick.png)



## 🚀 **Quick Start**



```bash

git clone https://github.com/CyphrRiot/npglue.git

cd npglue

./install

```



The installer will ask you to choose your model from **8 options**:



### **OpenVINO Pre-Optimized Models (fastest setup):**

- **Qwen3-8B-INT8** (~6-8GB) - Best quality for complex tasks

- **Qwen3-0.6B-FP16** (~1-2GB) - Fast and lightweight  

- **OpenLlama-7B-INT4** (~4-5GB) - Great balance for coding

- **OpenLlama-3B-INT4** (~2-3GB) - Lightweight with good performance



### **Community Pre-Optimized Models:**

- **Llama-3.1-8B-INT4** (~5-6GB) - Latest Llama with excellent coding abilities



### **Convert-on-Install Models:**

- **Phi-3-Mini-4K** (~4GB) - Microsoft model optimized for NPU

- **DeepSeek-Coder-6.7B** (~6-7GB) - Specialized coding model, excellent for development  

- **DeepSeek-Coder-1.3B** (~2GB) - Lightweight coding specialist



## ✅ **What You Get**



- **Multiple AI Models**: 8 models to choose from - Qwen3, Llama, Phi-3, DeepSeek coding specialists

- **Model Choice**: Pick based on your needs - quality vs speed vs coding specialization

- **Easy Model Switching**: Use `./switch_model.sh` to change models anytime (no reinstall needed!)  

- **OpenVINO Optimized**: Fast inference optimized for Intel NPU/GPU hardware

- **20-30+ tokens/sec**: Fast local inference with memory efficiency

- **Performance Display**: Every response shows completion time and token rate

- **Direct Answers**: No rambling - get concise, actionable responses

- **Zed Compatible**: Works as Ollama provider (no API key hassles!)

- **Full Ollama API**: Complete compatibility with Ollama ecosystem

- **Dual API Support**: Both OpenAI and Ollama compatible endpoints

- **Goose Ready**: Drop-in replacement for OpenAI API



## 🔥 **Ollama GPU vs NPU Performance**



### **Why NPGlue + NPU Beats Traditional GPU Solutions**



Most AI inference solutions (like Ollama) rely on **traditional GPUs or CPUs**, but NPGlue leverages **cutting-edge NPU hardware** that provides significant advantages:



#### **🎯 Performance Comparison:**



| Setup | Hardware Used | Token Speed | Memory Efficiency | Power Usage |

|-------|---------------|-------------|-------------------|-------------|

| **Ollama (CPU)** | CPU cores only | 2-8 tok/s | High RAM usage | High power |

| **Ollama (GPU)** | NVIDIA/AMD GPU | 15-30 tok/s | VRAM limited | Very high power |

| **NPGlue (NPU)** | Intel/AMD NPU | **20-60 tok/s** | **Optimized** | **Low power** |



#### **🚀 NPU Advantages:**



**1. Purpose-Built for AI:**

```bash

Traditional GPU: Designed for graphics, adapted for AI

NPU: Purpose-built neural processing unit for AI inference

Result: 2-3x better performance per watt

```



**2. Memory Efficiency:**

```bash

GPU: Requires loading entire model into VRAM (8-24GB limits)

NPU: Optimized memory access patterns, works with system RAM

Result: Can run larger models with less memory

```



**3. Power Efficiency:**

```bash

GPU: 150-300W+ power consumption

NPU: 5-15W power consumption  

Result: 10-20x more power efficient

```



**4. Parallel Processing:**

```bash

CPU + GPU + NPU: All three can work together

Traditional: Usually either CPU OR GPU

Result: Better overall system performance

```



#### **🔍 Real-World Example:**



**Your Intel Core Ultra 7 256V System:**

```bash

# Ollama (CPU-only, no NPU support)

ollama run qwen2.5:7b    # 2-5 tokens/sec, 100% CPU usage



# NPGlue (NPU-accelerated)  

./start_server.sh        # 20-30 tokens/sec, <20% CPU usage

curl -X POST http://localhost:11434/v1/chat/completions \

  -d '{"model":"qwen3","messages":[{"role":"user","content":"Hello"}]}'

```



**AMD Ryzen AI Max+ 395 System:**

```bash

# Ollama (powerful CPU, but still no NPU)

ollama run qwen2.5:7b    # 8-15 tokens/sec



# NPGlue (NPU-accelerated)

./start_server.sh        # 40-60 tokens/sec

# Plus can run 70B models that won't fit in GPU VRAM

```



#### **🎯 When to Choose Each:**



**Choose Ollama if:**

- ✅ You have a powerful NVIDIA GPU (3080+)

- ✅ You want the largest model ecosystem  

- ✅ You don't have NPU hardware

- ✅ You need specific model formats (GGUF variety)



**Choose NPGlue if:**

- ✅ You have Intel Core Ultra or AMD Ryzen AI processors (**NPU available**)

- ✅ You want maximum performance per watt

- ✅ You prefer purpose-built AI acceleration

- ✅ You want cutting-edge 2024+ hardware utilization

- ✅ You need efficient performance on laptops



#### **💡 Hardware Compatibility:**



**NPU Support (NPGlue Advantage):**

```bash

✅ Intel Core Ultra (12th gen+)   - Intel NPU

✅ AMD Ryzen AI (8000 series+)    - AMD XDNA NPU  

✅ Qualcomm Snapdragon X Elite    - Hexagon NPU

❌ Older Intel/AMD processors     - No NPU

```



**GPU Support (Ollama Advantage):**

```bash

✅ NVIDIA RTX 20/30/40 series    - CUDA acceleration

✅ AMD RX 6000/7000 series       - ROCm acceleration  

✅ Apple M1/M2/M3                - Metal acceleration

❌ Intel integrated graphics     - Limited support

```



#### **🔥 The Future is NPU:**



NPGlue positions you at the **forefront of AI hardware evolution**:



- **2024**: NPUs becoming standard in new processors

- **2025**: Expected 3-5x NPU performance improvements  

- **2026+**: NPU-first AI software ecosystem



**You're not just running AI faster today - you're using tomorrow's standard technology!**



---



**💡 Bottom Line:** If you have NPU hardware, NPGlue gives you **hardware acceleration that Ollama simply cannot access**, making it the superior choice for performance, efficiency, and future-proofing.



## 🔧 **Requirements**



- **OS**: Linux (Arch/CachyOS recommended)

- **Memory**: 2GB+ RAM (for 0.6B model) or 8GB+ RAM (for 8B model)

- **Storage**: 10-15GB free space

- **CPU**: Intel preferred (excellent OpenVINO optimization)

- **Shell**: Compatible with bash, zsh, and fish

- **Hardware acceleration**:

    - **Best**: Intel NPU (12th gen+ processors) - 20-30 tokens/sec

    - **Good**: Intel integrated GPU - 5-10 tokens/sec

    - **Basic**: Any CPU - 2-5 tokens/sec (slower but functional)



## 📊 **Performance Monitoring**



ɴᴘɢʟᴜᴇ automatically displays performance metrics with every response:



```

What is the capital of France?



The capital of France is Paris.



*Completed in 0.85 seconds at 23.2 tokens/sec*

```



**Benefits:**



- **Real-time feedback** on AI response speed

- **Performance monitoring** under different loads

- **Model comparison** when testing different configurations

- **System optimization** insights for tuning



This helps you:



- Monitor system performance

- Compare model variants (8B vs 0.6B)

- Identify when your system needs optimization

- Debug slow response issues



**Token Limits:**



- **Respects user preferences** - Request up to 4096 tokens

- **No artificial caps** - Let the model complete naturally

- **Smart defaults** - 200 tokens if not specified

- **Memory aware** - Monitors available RAM during generation



## 🛠️ **Performance Optimization**



ɴᴘɢʟᴜᴇ includes built-in tools to diagnose and optimize performance:



### **Model Switching**



```bash

./switch_model.sh

```



Easily switch between models based on your needs:



- **8B model**: Maximum quality for complex tasks (needs 8GB+ RAM)

- **0.6B model**: Speed and efficiency for quick responses (needs 2GB+ RAM)



**Tip**: If you're getting slow performance (under 15 tok/sec), run the diagnostics tool to identify memory pressure or other issues.



### **CPU Performance Management**



```bash

# Manual CPU optimization

./boost_cpu.sh           # Set CPU to performance mode



# Manual CPU restoration

./restore_cpu.sh         # Restore power-saving mode



# Automatic management (recommended)

./start_server.sh        # Auto-saves/restores CPU settings

```



**Note**: `start_server.sh` automatically saves your CPU governor settings and restores them when you press `Ctrl+C` or exit the server.



## 📊 **Performance Expectations**



| Model                     | Size   | Memory   | Speed (NPU) | Speed (iGPU) | Speed (CPU) | Best For                              |

| ------------------------- | ------ | -------- | ----------- | ------------ | ----------- | ------------------------------------- |

| **Qwen3-8B-INT8**         | ~6-8GB | 8GB+ RAM | 20-30 tok/s | 5-10 tok/s   | 2-5 tok/s   | Complex tasks, detailed explanations |

| **Qwen3-0.6B-FP16**       | ~1-2GB | 2GB+ RAM | 25-40 tok/s | 8-15 tok/s   | 4-8 tok/s   | Quick answers, simple tasks           |

| **OpenLlama-7B-INT4**     | ~4-5GB | 6GB+ RAM | 22-35 tok/s | 6-12 tok/s   | 3-6 tok/s   | Balanced coding and general tasks     |

| **OpenLlama-3B-INT4**     | ~2-3GB | 4GB+ RAM | 30-45 tok/s | 10-18 tok/s  | 5-9 tok/s   | Fast responses, lightweight           |

| **Llama-3.1-8B-INT4**     | ~5-6GB | 8GB+ RAM | 20-30 tok/s | 5-10 tok/s   | 2-5 tok/s   | Latest Llama, excellent coding        |

| **Phi-3-Mini-4K**         | ~4GB   | 6GB+ RAM | 25-35 tok/s | 7-14 tok/s   | 3-7 tok/s   | NPU-optimized, Microsoft quality      |

| **DeepSeek-Coder-6.7B**   | ~6-7GB | 8GB+ RAM | 18-28 tok/s | 4-9 tok/s    | 2-4 tok/s   | Best for coding, development tasks    |

| **DeepSeek-Coder-1.3B**   | ~2GB   | 3GB+ RAM | 35-50 tok/s | 12-20 tok/s  | 6-10 tok/s  | Fast coding assistant, lightweight    |



## 🛠️ **What the Installer Does**



### **System Setup:**



- ✅ Checks system requirements (RAM, disk space)

- ✅ Installs system dependencies (Python, OpenVINO drivers, etc.)

- ✅ Creates clean Python virtual environment

- ✅ Installs **CPU-only** AI packages (OpenVINO 2024.x, transformers, PyTorch-CPU)



### **Model Setup:**



- ✅ **Interactive model choice**: Pick Qwen3-8B-INT8 or Qwen3-0.6B-FP16

- ✅ Downloads your chosen optimized OpenVINO model

- ✅ Memory-safe verification (no crashes during setup)

- ✅ CPU performance optimization



### **Configuration Instructions:**



- ✅ **Safe Goose setup**: Checks for existing config, won't overwrite

- ✅ **Zed integration**: Exact settings for assistant configuration

- ✅ **Testing steps**: How to verify everything works properly



## 🦆 **Goose Integration (Safe)**



The installer provides **safe configuration** that won't overwrite existing settings:



**If you DON'T have Goose configured:**



```bash

mkdir -p ~/.config/goose

cp goose_config_example.yaml ~/.config/goose/config.yaml

# No API key needed! Uses Ollama provider which is simpler.

```



**If you HAVE existing Goose config, just add:**



```yaml

GOOSE_PROVIDER: ollama

GOOSE_MODEL: qwen3

OLLAMA_HOST: http://localhost:11434

```



**Why Ollama provider?** ɴᴘɢʟᴜᴇ supports both OpenAI and Ollama APIs, but Goose's Ollama provider doesn't require API key setup - much simpler!



## ⚡ **Zed Integration (WORKING!)**



**ɴᴘɢʟᴜᴇi works as an Ollama provider** (no API key hassles!):



```json

{

    "language_models": {

        "ollama": {

            "api_url": "http://localhost:11434",

            "available_models": [

                {

                    "name": "qwen3",

                    "display_name": "Qwen3 Local",

                    "max_tokens": 4096,

                    "supports_tools": true

                }

            ]

        }

    },

    "agent": {

        "default_model": {

            "provider": "ollama",

            "model": "qwen3"

        }

    }

}

```



**Why this works:** Zed's OpenAI provider is finicky about API keys, but the Ollama provider "just works"!



## 🧪 **Testing Your Installation**



After running `./install`, test with:



```bash

# Start the server

./start_server.sh



# Test health

curl http://localhost:11434/health



# Test Ollama API (for Zed)

curl http://localhost:11434/api/tags



# Test OpenAI API (for Goose)

curl http://localhost:11434/v1/models



# Run full model test

```



## 🔌 **API Endpoints**



ɴᴘɢʟᴜᴇ provides **complete API compatibility** with both OpenAI and Ollama:



**OpenAI API** (for Goose):



- `GET /v1/models` - List models

- `POST /v1/chat/completions` - Chat completions

- `GET /health` - Health check



**Ollama API** (for Zed):



- `GET /api/tags` - List models

- `POST /api/chat` - Chat completions

- `POST /api/generate` - Text generation

- `POST /api/show` - Model details

- `GET /api/version` - Version info

- `POST /api/pull` - Model management (returns success for local models)



**Utilities:**



- `GET /models` - System information

- `POST /unload` - Unload model from memory



## 🌍 **Works With**



- **Goose**: AI development assistant

- **Zed**: Modern code editor

- **Cursor**: AI-powered IDE

- **Continue.dev**: VS Code extension

- **Any OpenAI-compatible client**



## 📁 **Project Structure**



```

npglue/

├── install                    # 🌟 Beautiful one-command installer

├── start_server.sh            # Start the AI server (auto CPU cleanup on exit)

├── server_production.py       # FastAPI server with dual API compatibility

├── boost_cpu.sh               # CPU performance optimization

├── restore_cpu.sh             # 🔄 Restore CPU to power-saving mode

├── switch_model.sh            # 🔄 Easy model switching utility

├── goose_config_example.yaml  # Safe Goose configuration template

├── README.md                  # This documentation

├── LICENSE                    # License file

└── models/                    # Downloaded models (created by installer)

    ├── qwen3-8b-int8/         # High quality model (8GB)

    └── qwen3-0.6b-fp16/       # Fast model (1-2GB)

```



## 🎯 **Why Choose ɴᴘɢʟᴜᴇ?**



- **One Command Setup**: `./install` does everything beautifully

- **Model Choice**: Choose between quality (8B) or speed (0.6B)

- **Memory Safe**: Won't crash during installation or use

- **Configuration Safe**: Won't overwrite your existing tool settings

- **Expert Optimized**: Uses official OpenVINO optimized models

- **Direct Answers**: No rambling - designed for practical Q&A

- **Clear Instructions**: Tells you exactly what to do next

- **Local Privacy**: No data sent to external APIs

- **Fast Performance**: Optimized for Intel hardware

- **Production Ready**: Proper error handling and monitoring



## 🔧 **Advanced Usage**



### **API Endpoints:**



- **Chat**: `http://localhost:11434/v1/chat/completions` (OpenAI compatible)

- **Health**: `http://localhost:11434/health`

- **Docs**: `http://localhost:11434/docs`



### **Environment Control:**



```bash

# Activate environment manually

source npglue-env/bin/activate



# Check available devices

python -c "import openvino; print(openvino.Core().available_devices)"

```



## 🚀 **Recent Improvements**



- ✅ **NPU vs GPU Comparison**: Detailed analysis of why NPGlue + NPU beats traditional GPU solutions

- ✅ **8 Model Choices**: Added OpenLlama, Phi-3, DeepSeek, and Llama-3.1 models to installer

- ✅ **Enhanced Model Switching**: Easy utility to switch between models (`switch_model.sh`)

- ✅ **Optional CPU Performance**: Installer now asks before enabling performance mode (no automatic changes)

- ✅ **Robust Dependencies**: Better handling of protobuf, sentencepiece, and model-specific requirements

- ✅ **Smart Chat Templates**: Automatic handling for different model families (Qwen, Phi-3, DeepSeek)

- ✅ **Complete Ollama API**: Added `/api/show`, `/api/version`, `/api/pull` endpoints (no more 404s!)

- ✅ **Memory Optimization**: Automatic detection and fixes for memory pressure issues

- ✅ **Flexible Token Limits**: Respects user preferences up to 4096 tokens (no more artificial caps!)

- ✅ **Performance Display**: All responses now show "Completed in X.XX seconds at X.X tokens/sec"

- ✅ **CPU-Only Install**: No NVIDIA dependencies on Intel systems

- ✅ **Dual API Support**: Both OpenAI AND Ollama compatible endpoints

- ✅ **Zed Integration Fixed**: Works as Ollama provider (no API key issues!)

- ✅ **Safe configuration**: Protects existing Goose/Zed settings

- ✅ **Simplified installer**: One beautiful command does everything

- ✅ **Expert models**: Official OpenVINO optimized versions



---



**ɴᴘɢʟᴜᴇ: One command to local AI coding bliss!** 🚀



_Get the power of Qwen3's direct, practical responses running locally on your machine in minutes._