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ThinkSystem SR670 Rack Server
- Up to 4 best-of-breed GPUs for accelerated performance
- Scalable cluster solution for AI and HPC workloads, supporting the LiCO platform
- Perfect balance of performance, density, and TCO for AI & GPU-intensive workloads
- Supports high-speed Mellanox EDR InfiniBand, Intel 2x 10GbE & Intel 2x 1GbE networking
- Supports standard SATA HDDs & SSDs, and M.2 boot SSDs for even greater storage flexibility
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Accelerating AI workloads
The Lenovo ThinkSystem SR670 delivers optimal performance for Artificial Intelligence (AI) and high performance computing (HPC). Supporting up to either four large, or eight small GPUs per 2U node, it is suited for the computationally intensive workload requirements of both Machine Learning, Deep Learning, and Inference.
Built on the latest Intel® Xeon® processor Scalable family CPUs and designed to support high-end GPUs including NVIDIA Tesla V100 and T4, the ThinkSystem SR670 delivers optimized accelerated performance for AI and HPC workloads.
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Maximum performance
As more workloads leverage the performance of accelerators, the demand for GPU density increases. Industries such as retail, financial services, energy, and healthcare are leveraging GPUs to extract greater insights and drive innovation utilizing ML, DL, and Inference techniques.
The ThinkSystem SR670 provides an optimized enterprise-grade solution for deploying accelerated HPC and AI workloads in production, maximizing system performance while maintaining data center density.
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Solutions that scale
Whether you’re just starting with AI or moving into production, your solution must scale with your organization's needs.
Working with Lenovo Intelligent Computing Orchestration (LiCO), Lenovo’s powerful cluster management platform for HPC and AI, the ThinkSystem SR670 can be used in a cluster with high-speed fabric to scale out as your demands increase. LiCO also provides workflows for both AI and HPC, and supports multiple AI frameworks, including TensorFlow, Caffe, allowing you to leverage a single cluster for diverse workload requirements.