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GPU Servers

GPU servers are rack-mounted servers designed to accelerate compute-intensive workloads with one or more graphics processing units. Compared with CPU-only servers, GPU servers provide higher parallel processing performance for AI training, machine learning, HPC, data analytics, scientific computing, rendering, and other GPU-accelerated applications.

When choosing a GPU server, compare the GPU model and quantity, GPU memory, PCIe layout, CPU and system memory, storage, networking, power supply, and cooling design. The right configuration depends on whether your priority is multi-GPU computing, GPU performance, expansion capacity, or deployment flexibility. Check the exact factory configuration to confirm whether the GPU, storage, power supplies, and other components are included.

GPU Server Models by Configuration Priority

Compare representative GPU server models by GPU density, computing capability, and expansion flexibility before reviewing the full product listing, current pricing, and configuration details.

Configuration Priority Representative Models Key Differences to Compare
High-Density Multi-GPU G5500 V5
G5500 V6
G5500 V7
Compare GPU quantity, GPU form factor, PCIe topology, GPU interconnect, power supply capacity, and cooling design.
GPU Computing and HPC G530 V5
G560 V5
Compare GPU computing capability, processor and memory configuration, network bandwidth, storage performance, and sustained cooling.
GPU Expansion and Scalability G5500 V6
G5500 V7
Compare memory capacity, PCIe slots, storage options, networking, power requirements, and future GPU upgrade flexibility.

What to Check Before Choosing a GPU Server

Confirm the supported GPU model and quantity, GPU memory, auxiliary power requirements, PCIe slot layout, CPU and memory configuration, storage and networking options, power redundancy, cooling capacity, operating system support, and GPU driver compatibility. Multi-GPU deployments should also be checked for rack space, power availability, airflow, and the exact factory configuration of the selected SKU.

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