High-Performance Computing (HPC) server clusters power cutting-edge AI, simulations, and data-intensive workloads. Whether you're deploying machine learning models, financial simulations, or scientific research, a well-planned HPC infrastructure ensures maximum performance and cost efficiency.
Refurbished HPC Servers and Server Parts
This article provides a step-by-step approach, covering:
Hardware selection (GPUs, CPUs, memory, storage, networking)
Performance metrics (teraflops, bandwidth, latency)
Refurbished vs. new options for cost efficiency
Deployment and optimization best practices
Step 1: Define HPC Server Cluster Requirements
Before choosing hardware, outline your workload needs.
Key Questions to Answer:
✔ What is the primary use case? (AI, simulations, rendering, etc.)
✔ How much compute power do you need? (FLOPS, core count, memory bandwidth)
✔ Will you run workloads on-premise or use cloud HPC?
✔ What is your budget, and are you open to refurbished hardware?
✔ How will you handle cooling and power requirements?
HPC Workload Type | Recommended Hardware |
Deep Learning & AI | NVIDIA H100, A100, AMD MI300 |
Scientific Computing | Intel Xeon Platinum, AMD EPYC, NVIDIA A40 |
Financial Modeling | NVIDIA A30, V100, or AMD Instinct GPUs |
Rendering & Simulation | RTX 6000 Ada, Quadro GPUs |
Step 2: Select Compute Hardware for HPC Server Clusters
1. CPU Selection: Intel vs. AMD
HPC clusters require high core count CPUs optimized for parallel processing.
Feature | Intel Xeon | AMD EPYC |
Max Cores | 60 (Xeon Sapphire Rapids) | 128 (EPYC Genoa) |
Memory Bandwidth | 480 GB/s | 460 GB/s |
PCIe Lanes | 80+ | 128 |
Power Efficiency | Lower | Higher |
HPC Use Cases | Scientific computing, traditional workloads | AI, data analytics, virtualization |
✔ Best choice: AMD EPYC for AI & data workloads, Intel Xeon for traditional HPC.
2. GPU Selection: NVIDIA vs. AMD
GPUs significantly accelerate AI, ML, and parallel workloads.
GPU Model | Memory | TFLOPS (FP64) | Bandwidth | Use Case |
NVIDIA H100 | 80GB HBM3 | 60 TFLOPS | 3.3 TB/s | AI/ML, HPC |
NVIDIA A100 | 40/80GB HBM2 | 19.5 TFLOPS | 2.0 TB/s | AI, cloud HPC |
AMD MI300 | 128GB HBM3 | 47 TFLOPS | 5.3 TB/s | AI, simulations |
NVIDIA A40 | 48GB GDDR6 | 7.1 TFLOPS | 696 GB/s | Visualization, VDI |
RTX 6000 Ada | 48GB GDDR6 | 5.8 TFLOPS | 960 GB/s | Engineering, CAD |
✔ Best choice: NVIDIA H100 for AI, AMD MI300 for memory-heavy workloads.
3. Memory Considerations
✔ Use ECC DDR5 RAM for reliability.
✔ AI workloads need 512GB+ RAM per node.
✔ Scientific computing prefers 1TB+ RAM for large datasets.
Step 3: Storage & Networking for HPC Server Cluster
1. Storage Solutions
✔ NVMe SSDs are essential for high-speed data access.
✔ Use parallel file systems (Lustre, GPFS) for scalability.
✔ Hybrid setups combine SSD caching + HDD storage for cost savings.
Storage Type | Read Speed | Write Speed | Best for |
NVMe SSD (PCIe 4.0) | 7,000 MB/s | 6,500 MB/s | AI/ML, DBs |
SAS SSD (12Gb/s) | 2,100 MB/s | 1,800 MB/s | Mixed workloads |
HDD (SAS 12Gb/s) | 250 MB/s | 250 MB/s | Bulk storage |
✔ Best choice: NVMe SSD for compute nodes, SAS SSDs for storage nodes.
2. Networking for HPC
✔ InfiniBand HDR (200Gb/s) or NDR (400Gb/s) for low latency.
✔ Ethernet 100GbE for cost-effective setups.
✔ Use RoCE (RDMA over Converged Ethernet) for lower CPU overhead.
Network Type | Speed | Latency | Use Case |
InfiniBand NDR | 400Gb/s | <1μs | AI, real-time HPC |
InfiniBand HDR | 200Gb/s | <2μs | General HPC |
Ethernet 100GbE | 100Gb/s | 10μs | Budget-friendly |
✔ Best choice: InfiniBand NDR for top-tier AI, 100GbE for budget setups.
Step 4: Power, Cooling & Cluster Management for HPC Server Cluster
✔ HPC racks require 3-phase power (208V or 400V).
✔ Liquid cooling is essential for high-density GPU clusters.
✔ Use SLURM, Kubernetes, or OpenStack for job scheduling.
✔ Best choice: Immersion cooling for AI clusters, air cooling for standard HPC.
Step 5: Optimize Costs with Refurbished HPC Server Cluster Hardware
Enterprises can reduce costs by up to 80% using refurbished servers. Ensure:
✔ Minimum 3-year warranty
✔ Tested performance benchmarks
✔ Trusted supplier
✔ No upfront payment—test first, pay later
✔ Fast availability—avoid long vendor lead times
Recommended Refurbished Models:
✔ Dell PowerEdge XE8545 (4U, AMD EPYC, NVIDIA A100/H100, NVLink)
Best for AI/ML training, deep learning, and simulations
Up to 4x NVIDIA A100 80GB or H100 GPUs, delivering over 3.2 petaflops FP16 compute
Direct NVLink support for ultra-fast GPU communication
✔ HPE Apollo 6500 Gen10 Plus (4U, HPC & AI GPU Server)
Designed specifically for HPC and AI clusters
Up to 8x NVIDIA A100, H100, or AMD Instinct MI250X GPUs
Dual AMD EPYC 7003/9004 or Intel Xeon Scalable CPUs
High-bandwidth PCIe 4.0/5.0 with liquid cooling options
✔ Lenovo ThinkSystem SR670 V2 (GPU-Dense HPC Node, 2U)
Perfect for AI supercomputing and scientific research
Supports up to 8x NVIDIA H100, A100, or AMD MI250X GPUs
Designed for scalable HPC clusters, optimized for FLOPS-intensive workloads
✔ Supermicro 9029GP-TNVRT (8x GPU, 10 PetaFLOPS AI Training Server)
One of the highest teraflops per U solutions for HPC
Up to 10 petaflops FP16 compute with NVIDIA H100/A100 GPUs
Optimized for AI training, CFD, and large-scale simulations
Step 6: Deployment & Benchmarking of HPC Server Clusters
✔ Install Rocky Linux, AlmaLinux, or Ubuntu LTS.
✔ Use LINPACK & MLPerf to benchmark performance.
✔ Tune BIOS for HPC mode (disable C-states, enable NUMA).
Refurbished HPC Servers and Server Parts
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