Node Requirements
Node Types and Specifications
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.
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.
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.
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.
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.
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