diff --git a/docs/docs/docs/articles/nitro.md b/docs/docs/docs/articles/nitro.md
index 877a4c032..1117aa75c 100644
--- a/docs/docs/docs/articles/nitro.md
+++ b/docs/docs/docs/articles/nitro.md
@@ -1,6 +1,93 @@
 ---
 sidebar_position: 1
-title: Nitro
+title: Why Nitro? The Inference Engine Behind Jan
 ---
 
-TODO
+Delve into Nitro, a robust inference engine that powers Jan. Nitro is a dedicated "inference server" crafted in C++, optimized for edge deployment, ensuring reliability and performance.
+
+⚡ Explore Nitro's codebase: [GitHub](https://github.com/janhq/nitro)
+
+## Problems of AI services
+
+Everyone wants to build their AI app, but they have a few challenges below.
+
+1. **No privacy**
+
+If you use an AI API like OpenAI ChatGPT, just say goodbye to privacy already.
+
+2. **Cumbersome integration**
+
+Let say you already have some interesting libraries for local AI, it's still very cumbersome to integrate.
+
+If you want to use something cutting edge like [llama-cpp](https://github.com/ggerganov/llama.cpp) you need to know a bit of CPP.
+
+And many other reaons.
+
+3. **Not standardized interface**
+
+Let say you solved the above points, you will still have issues with non-standard interface, you cannot re-use other projects code.
+
+
+## Benefits of Using Nitro:
+
+1. **Compact Binary Size**:  
+   - Nitro's efficient binary size: ~3mb compressed on average
+     ![](https://hackmd.io/_uploads/Sy_IYU8GT.png)
+
+2. **Streamlined Deployment**:  
+   Nitro is designed for ease of deployment. Simply download and execute. Note the requirement for specific hardware dependencies, like CUDA.
+
+3. **User-Friendly Interface**:  
+   Nitro offers a straightforward HTTP interface. With compatibility for multiple standard APIs, including OpenAI formats, integration is hassle-free.
+
+   ```bash
+   curl --location 'http://localhost:3928/inferences/llamacpp/chat_completion' \
+        --header 'Content-Type: application/json' \
+        --header 'Accept: text/event-stream' \
+        --header 'Access-Control-Allow-Origin: *' \
+        --data '{
+           "messages": [
+               {"content": "Hello there 👋", "role": "assistant"},
+               {"content": "Can you write a long story", "role": "user"}
+           ],
+           "stream": true,
+           "model": "gpt-3.5-turbo",
+           "max_tokens": 2000
+        }'
+   ```
+
+4. **Seperated process**:  
+   Nitro operates independently, ensuring no interference with your main application processes. It self-manages, allowing you a focused development environment.
+
+5. **Broad Compatibility**:  
+   Nitro supports a variety of platforms including Windows, MacOS, and Linux, and is compatible with arm64, x86, and NVIDIA GPUs, ensuring a versatile deployment experience.
+   
+6. **Multi-threaded and performant by default***
+    Built on DrogonCPP, a very fast CPP web framework in CPP. 
+
+## Getting Started with Nitro:
+
+**Step 1: Obtain Nitro**:  
+Access Nitro binaries from the release page to begin.  
+🔗 [Download Nitro](https://github.com/janhq/nitro/releases)
+
+**Step 2: Source a Model**:  
+For those interested in the llama C++ integration, obtain a "GGUF" model from The Bloke's repository.  
+🔗 [Download Model](https://huggingface.co/TheBloke)
+
+**Step 3: Initialize Nitro**:  
+Launch Nitro and position your model using the following API call:
+
+```bash
+curl -X POST 'http://localhost:3928/inferences/llamacpp/loadmodel' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "llama_model_path": "/path/to/your_model.gguf",
+    "ctx_len": 2048,
+    "ngl": 100,
+    "embedding": true
+  }'
+```
+
+**Step 4: Engage with Nitro**:  
+Interact with Nitro to evaluate its capabilities. With its alignment to the OpenAI format, you can anticipate consistent and reliable output.