Hardware Specifications
Hardware Specifications
Swarm’s Hardware Specifications provide detailed guidance on the minimum and recommended configurations for various node types, ensuring optimal performance for specific AI workloads. Each node type is designed to address a distinct use case within the decentralized infrastructure.
Node Type
Minimum Specs
Recommended Specs
Optimal Use Case
AI Training
8x NVIDIA A100 GPUs, 512GB RAM
16x NVIDIA A100 GPUs, 1TB RAM
Large-scale model training, distributed deep learning workloads.
Inference
4x NVIDIA T4 GPUs, 128GB RAM
8x NVIDIA A10 GPUs, 256GB RAM
High-throughput model serving for real-time predictions.
General Compute
32 CPU cores, 128GB RAM
64 CPU cores, 256GB RAM
Data processing, orchestration, and lightweight AI workloads.
Storage
2TB NVMe SSD, 10Gbps network
10TB NVMe SSD, 100Gbps network
Data storage for training datasets, model checkpoints, and archival purposes.
Key Features
Tailored Configurations:
Each node type is optimized for a specific workload, ensuring efficient resource utilization.
Scalability:
Nodes can be scaled horizontally (adding more nodes) or vertically (upgrading specs) to meet workload demands.
High Performance:
High-end configurations provide the compute, memory, and storage required for intensive AI tasks.
Low Latency:
Storage nodes with high-speed NVMe SSDs and fast network connections enable quick access to data.
Benefits
Efficiency: Optimized configurations reduce operational overhead and maximize throughput for AI workloads.
Reliability: High-performance hardware ensures consistency and stability under load.
Flexibility: Supports a range of workloads, from lightweight inference to large-scale training.
Future-Proofing: Recommended specs are designed to accommodate evolving AI models and data requirements.
These Hardware Specifications ensure Swarm’s infrastructure is capable of delivering exceptional performance across diverse AI applications, making it a robust and scalable platform for distributed AI workloads.
Last updated