# Node Requirements

**Node Types and Specifications**

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1. **GPU Nodes**:
   * **Purpose**: Optimized for AI workloads requiring high-performance GPUs, such as training and inference.
   * **Use Cases**:
     * **AI Training**: Handles compute-intensive tasks like deep learning model training.
     * **Inference**: Processes real-time predictions and fine-tuning tasks.
   * **Specifications**:
     * **Minimum**: NVIDIA T4, 16GB VRAM, 64GB system RAM.
     * **Recommended**: NVIDIA A100, 80GB VRAM, 128GB system RAM, NVMe SSD.
2. **CPU Nodes**:
   * **Purpose**: Designed for general-purpose compute tasks, including orchestration, lightweight AI workloads, and preprocessing.
   * **Use Cases**:
     * **General Compute**: Runs lightweight tasks and supports distributed workloads.
     * **Memory Optimized**: Handles tasks requiring significant system memory.
   * **Specifications**:
     * **Minimum**: 4 CPU cores, 8GB RAM.
     * **Recommended**: 32 CPU cores, 128GB RAM.
3. **Storage Nodes**:
   * **Purpose**: Provides scalable and high-capacity storage for datasets, model checkpoints, and logs.
   * **Use Cases**:
     * **High Capacity**: Supports archival and long-term data storage.
     * **High Performance**: Enables fast access to training data and intermediate results.
   * **Specifications**:
     * **Minimum**: 1TB HDD, 100GB SSD.
     * **Recommended**: 10TB HDD, 2TB NVMe SSD.

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**Key Features**

* **Flexibility**:
  * Accommodates a variety of workloads with GPU, CPU, and storage-optimized nodes.
* **Scalability**:
  * Supports seamless addition of nodes to meet growing demand for AI workloads.
* **High Performance**:
  * Ensures efficient execution of training, inference, and data processing tasks.

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**Benefits**

* **Efficiency**: Tailored nodes maximize resource utilization for specific tasks.
* **Reliability**: Robust hardware specifications ensure consistent performance across workloads.
* **Scalability**: Nodes can be upgraded or scaled horizontally to accommodate more complex workloads.
* **Cost Optimization**: Allows providers to contribute resources based on their strengths, optimizing overall operational costs.

Swarm’s **Node Requirements** ensure a balanced and efficient infrastructure capable of supporting diverse AI tasks while delivering high performance and reliability.