Hardware Configurations

Hardware Configurations

Swarm offers a range of hardware configurations tailored to different AI workloads, balancing performance, cost, and scalability to meet diverse user needs.

Configuration	Specs	Use Case	Performance
Standard	8x NVIDIA A100 GPUs	Large-scale model training for deep learning.	1000 TFLOPS (FP32)
High Memory	16x NVIDIA A100 GPUs, 2TB RAM	Distributed training of extremely large models.	2000 TFLOPS (FP32)
Economy	4x NVIDIA T4 GPUs	Cost-effective fine-tuning and lightweight model training.	100 TFLOPS (FP32)

Key Features

• Standard Configuration:

  • Designed for most large model training tasks, providing a balance of performance and resource utilization.

  • Ideal for deep learning and complex AI applications.

• High Memory Configuration:

  • Equipped for distributed training of very large models that require extensive GPU and memory resources.

  • Optimized for workloads like transformer-based language models (e.g., GPT, BERT).

• Economy Configuration:

  • Cost-efficient setup for smaller-scale fine-tuning tasks or inference workloads.

  • Suitable for startups and researchers working with budget constraints.

Benefits

• Flexibility: Configurations support a wide range of AI/ML tasks, from fine-tuning to full-scale distributed training.

• Scalability: Hardware resources can be scaled up or down based on workload requirements.

• Performance: High-end configurations deliver top-tier computational performance for demanding applications.

Swarm’s hardware configurations provide users with powerful, customizable options to accelerate their AI development and deployment workflows efficiently.