Merge pull request #255 from DevEmilio96/main

Hardware Requirements

README.md

@@ -77,6 +77,22 @@ The current recommended way to install exo is from source.
   - CUDA (https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#cuda-cross-platform-installation) (test with `nvcc --version`)
   - cuDNN (https://developer.nvidia.com/cudnn-downloads) (test with [link](https://docs.nvidia.com/deeplearning/cudnn/latest/installation/linux.html#verifying-the-install-on-linux:~:text=at%20a%20time.-,Verifying%20the%20Install%20on%20Linux,Test%20passed!,-Upgrading%20From%20Older))
 
+### Hardware Requirements
+
+| Component          | MLX Requirements                                              | TinyGrad Requirements (for Llama-3.1-8B or similar)                    |
+|--------------------|---------------------------------------------------------------|------------------------------------------------------------------------|
+| **CPU**            | Apple Silicon (M1, M2, or later) only                         | Minimum: Intel Core i7-12700 or AMD Ryzen 7 5800X <br>Recommended: Intel Core i9-12900K or AMD Ryzen 9 5900X |
+| **GPU**            | Apple Silicon Integrated GPU                                  | Minimum: NVIDIA RTX 4070 (12 GB VRAM) <br>Recommended: NVIDIA RTX 4080 (16 GB VRAM)  |
+| **RAM**            | Minimum: 16 GB <br>Recommended: 32 GB                         | Minimum: 32 GB <br>Recommended: 64 GB                                  |
+| **Storage**        | Minimum: 256 GB SSD <br>Recommended: 512 GB SSD               | Minimum: 512 GB SSD <br>Recommended: 1 TB SSD                          |
+| **Operating System**| macOS (Big Sur)                               | Ubuntu                                              |
+
+**Note**:  
+- For **MLX**, you can currently run **smaller models** such as **Llama-3.2-1B**, which are optimized for Apple Silicon hardware.
+- For **TinyGrad**, the **smallest model** currently supported is **Llama-3.1-8B**, which requires more robust hardware to run effectively.
+- **Hardware requirements are indicative**: The overall load is distributed across the **CPU, RAM**, and **GPU/VRAM**, not solely on the GPU. Therefore, your system's performance depends on its ability to handle this distribution effectively.
+- It is also **possible to run models in a cluster mode**, utilizing multiple devices to distribute the computation load across multiple machines or GPUs, enhancing performance.
+
 ### From source