Supermicro GPU servers for AI in Vietnam

1. What is a GPU server, and how is an AI server different?

A GPU server is a server designed to host multiple GPUs, with the power delivery, cooling and PCIe or NVLink connectivity needed to run them at full capacity. Most NVIDIA GPU servers today use PCIe GPUs or the HGX platform with SXM GPUs; rack-scale systems such as GB300 NVL72 use Grace Blackwell superchips. Unlike a general-purpose server, a GPU server is usually limited by GPU memory, GPU-to-GPU bandwidth, power and cooling rather than by the CPU.

An AI server describes the purpose: a GPU server that has been configured with a software platform (drivers, containers, inference engines, orchestration tools) and is ready to run AI workloads. In short, the GPU server is the hardware; the AI server is the hardware plus the software layer and the way it is operated.

For a detailed explanation of the concept and when a business needs to invest: What is a GPU server and when do you need one? (Vietnamese).

2. Choosing a GPU server by AI workload

The fastest way to choose is to start from the workload, not from the GPU name. The three workload groups below map to three groups of Supermicro AI servers with different architectures (Figure 1).

Reference systems

NVIDIA AI servers: when to move up to HGX

If the model fits in one or two GPUs, a multi-GPU PCIe server running several replicas in parallel is usually a sensible starting point. When a model must be split across GPUs (tensor parallelism) or you need to train, NVLink with NVSwitch on the HGX platform becomes important, because the PCIe path turns into the bottleneck. According to Supermicro product pages, memory per GPU on HGX platforms is 141 GB (H200), 180 GB (B200) and 288 GB (B300).

3. Networking for GPU clusters

A single GPU server can run on your existing enterprise network. Once you build a cluster, the server-to-server (east–west) network decides whether GPUs sit idle waiting for data: training clusters typically use 400–800G adapters per GPU and a rail-optimized design, while small inference clusters need less. Choose between InfiniBand and Ethernet based on scale, your operations team and existing infrastructure. For example, the SYS-822GS-NB3RT comes with eight 800GbE OSFP ports using NVIDIA ConnectX-8 SuperNICs.

Read more: GPU cluster networking: InfiniBand or Ethernet (Vietnamese).

4. Storage for GPU servers

Fast GPUs need data to arrive on time. For inference, watch the KV cache when contexts are long and users are many; for training, watch dataset read speed and checkpoint writes. CSC designs storage in tiers: local NVMe on the GPU servers, all-flash storage servers for hot data, and large-scale S3 object storage with Cloudian HyperStore (which supports NVIDIA GPUDirect for S3).

• KV cache storage tiering for AI inference: from HBM to S3 (Vietnamese)
• 15 FAQs about object storage with NVIDIA GPUDirect (Vietnamese)
• AI data lake on Supermicro storage servers (Vietnamese)
• Storage for AI: object, file or block storage (Vietnamese)
• Storage Solution for AI & HPC

5. Software and operations for AI servers

Hardware only creates value once there is a platform layer that lets many teams use GPUs safely and efficiently. In the CSC ecosystem, this layer includes:

• Inference engines such as vLLM and SGLang: vLLM or SGLang: which engine should enterprises choose (Vietnamese).
• Rafay GPU PaaS to share GPUs across departments with quotas and self-service: Sharing Supermicro GPU servers across departments with Rafay (Vietnamese) and the Rafay product page.
• NVIDIA AI Enterprise for a vendor-supported AI software suite.

6. Buy GPU servers or rent GPUs?

Buying on-premises GPU servers and renting GPU cloud serve different needs, and many organizations use both. Rather than comparing prices, compare against one framework: expected GPU utilization over three years, data location and sovereignty requirements, power, cooling and data center costs, operations staff, and how much flexibility you need when demand changes. Steady workloads and sensitive data usually favor on-premises investment; short-term experiments or highly variable workloads usually suit on-demand rental.

Full framework: Buy GPU servers or rent GPU cloud: a TCO framework (Vietnamese).

7. CSC services when you invest in GPU servers

Supermicro offers a broad range of GPU servers, optimized per workload, with air- and liquid-cooling options. CSC adds what you need to put those servers into real production:

Service details: CSC AI infrastructure deployment services.