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In corporate AI projects, the "right" accelerator is chosen not only based on the price list and specifications. Late pilot deadlines, unstable inference under load, controversial support conditions, and software and licensing restrictions are almost always more expensive than the difference between two seemingly similar graphics cards. With the NVIDIA H200 PCIe, the situation has become particularly ambiguous. This primarily refers to the NVIDIA H200 NVL 141 GB PCIe Passive GPU cards, which are available on the market in both original and modified OEM versions. However, these are essentially different products, and the differences become apparent not at the initial launch but later on, when the infrastructure needs to be maintained, updated, scaled, and managed for SLA purposes. Native NVIDIA H200 PCIe vs OEM Adapted Solutions Anatomy of two options: native NVIDIA H200 PCIe and adapted SXM module in PCIe format The original NVIDIA H200 in the PCIe form factor is a complete server card where thermal design, power, mechanics, and firmware are initially designed to work in standard PCIe platforms. Around such accelerators, server vendors form supported configurations, and the customer gets a clear division of responsibility between the GPU manufacturer, platform, and integrator. Under the "OEM version" of the H200 PCIe, there is a different approach: the SXM module, designed for HGX systems, is physically transferred to an adapter board for installation in a PCIe slot. At the basic level, you can use the same GPU, so the cards may appear to be equivalent in terms of functionality and performance. However, as we use the cards, differences begin to emerge due to the lack of an official NVIDIA warranty and engineering compromises in cooling and power supply. SXM modules are intended to operate as part of HGX systems with centralized cooling and a heat package of up to 700 watts under typical load. In contrast, the original H200 PCIe cards operate at 600 watts and use a different heat dissipation profile in standard server platforms. When engineers convert an SXM module to a PCIe format, these differences in thermal design can cause overheating, throttling, and reduced stability under long-term loads. Lynx 1. - Adapter card from SXM to PCIe  A separate difference is related to the software stack. For the original NVIDIA H200 NVL in PCIe-design, there is a five-year subscription to NVIDIA AI Enterprise Software (NVAIE), which directly affects the operation model. In this case, the customer receives not only an accelerator, but also access to a supported corporate AI platform with formalized support and updates, which significantly reduces the risks of building and scaling production infrastructure. NVIDIA AI Enterprise (NVAIE) as a Key Differentiator The value of the NVAIE software stack If the choice of H200 PCIe was limited to “pure” computing, it would indeed be more about supply and price. However, in enterprise AI, the key difference between the original cards is the inclusion of NVIDIA AI Enterprise. This is not just a license, but a supported software framework for industrial use, which immediately sets a different level of predictability and responsibility compared to bare hardware. In practice, two approaches to inference collide here. vLLM provides maximum flexibility, but requires a mature team: environment selection, compatible drivers and CUDA, optimization settings, monitoring, updates and security. For teams where AI practice is just being formed, this often turns into a bottleneck. NVIDIA NIM closes another task: these are supported containerized microservices for inference, optimized for specific GPUs. Their value lies not in the delivery format, but in the speed of service deployment and the reduction of operational risks through fixed configurations, updates, and scalability. NVIDIA MIG Technology for Efficient GPU Partitioning This architecture is complemented by NVIDIA MIG (Multi-Instance GPU) technology, which is a hardware mechanism for splitting a single physical accelerator into multiple isolated instances. In the case of the H200, a single GPU can be split into up to 7 independent MIG instances, each with its own compute units, memory, and guaranteed resource isolation. Fig. 2 - NVIDIA MIG Overview In practice, this means that instead of one large "monolithic" service, you can run multiple separate inference loops. For example, different small models with up to 8B parameters, such as LLaMA 3.1-8B or Mistral-8B, are placed in their own MIG partitions and do not compete with each other for resources. The load of one model does not affect the stability of the neighboring model. NVIDIA H200 MIG Profiles and GPU Partitioning Table   MIG Profile Memory Share SM Share Hardware Units L2 Cache Copy Engines Max Instances MIG 1g.18gb 1/8 1/7 1 NVDEC / 1 JPEG / 0 OFA 1/8 1 7 MIG 1g.18gb + media extensions 1/8 1/7 1 NVDEC / 1 JPEG / 1 OFA 1/8 1 1 (media extension available for only one 1g profile) MIG 1g.35gb 1/4 1/7 1 NVDEC / 1 JPEG / 0 OFA 1/8 1 4 MIG 2g.35gb 2/8 2/7 2 NVDEC / 2 JPEG / 0 OFA 2/8 2 3 MIG 3g.71gb 4/8 3/7 3 NVDEC / 3 JPEG / 0 OFA 4/8 3 2 MIG 4g.71gb 4/8 4/7 4 NVDEC / 4 JPEG / 0 OFA 4/8 4 1 MIG 7g.141gb Full capacity 7/7 7 NVDEC / 7 JPEG / 1 OFA Full capacity 8 1 Table 1 - GPU Instance Profiles on NVIDIA H200   From Pilot to Production AI Infrastructure From pilot to industrial operation Thanks to the fact that only original PCIe cards from NVIDIA are used in ITPOD servers from the AI/ML Computing series, ITGLOBAL.COM as a cloud service provider offers customers all the benefits of NVIDIA AI Enterprise Software. Customers get maximum performance and resource utilization, including support for the latest optimizations and technologies. In combination, this gives a predictable SLA, transparent audit and minimization of risks when scaling corporate AI. text_with_btn btn="Leave a request" id="50"Get a consultation on AI Infrastructure/text_with_btn