NVIDIA H200 SXM 141 GB

The NVIDIA H200 SXM 141 GB is a data-center accelerator built on the Hopper architecture, featuring the GH100 chip fabricated on TSMC’s 5 nm process. It is positioned as a high-end server component, evidenced by its SXM module form factor, lack of display outputs, and production status remaining active. As a compute-oriented part, its benchmark percentile ranks it in the 50th percentile against all GPUs, indicating it is a mid-pack performer in a database that includes both consumer and enterprise parts, though its specifications clearly target professional workloads rather than gaming.

Who Should Consider It

The H200 SXM 141 GB is not designed for any consumer or enthusiast gaming scenario. With no display outputs, it cannot drive a monitor directly, and its architecture is optimized for computational throughput rather than rasterization. The data shows a pixel rate of 47.52 GPixel/s and a texture rate of 1,045.4 GTexel/s; while these figures are substantial, they are secondary to the card’s compute capabilities. The absence of a DirectX, OpenGL, or Vulkan API listing in the specifications reinforces that this is a compute-first product, not a rendering solution.

Consequently, this accelerator should be considered by organizations running large-scale AI training, scientific simulations, or high-performance computing tasks where memory capacity and bandwidth are critical. The 141 GB of HBM3e memory is the defining feature here, making it suitable for models or datasets that exceed the VRAM capacity of typical accelerators. For anyone seeking a graphics card for gaming or workstation rendering, this unit is unsuitable; for those managing server clusters focused on FP32 or FP16 workloads, the specifications indicate it is a viable candidate. The 50th percentile ranking suggests that while it is not the absolute fastest in the database, it offers a balanced profile that may appeal to users needing a specific combination of memory size and compute power.

Memory Subsystem

The memory subsystem is the H200’s most distinctive characteristic. It is equipped with 141 GB of HBM3e memory, a capacity that dwarfs most other accelerators in the database. This is paired with a 6144-bit memory bus, which is exceptionally wide, and the effective memory clock runs at 6.4 Gbps. The result is a memory bandwidth of 4.89 TB/s, a figure that enables rapid data movement to and from the compute cores.

For high-resolution or large-batch workloads, this bandwidth is critical. In AI inference or training, having 141 GB of on-board memory means that large transformer models can be loaded entirely into VRAM, avoiding the performance penalty of spilling to system memory over the PCIe bus. The 4.89 TB/s bandwidth ensures that the 16,896 shading units and 528 tensor cores are fed with data at a rate that minimizes idle time. In scenarios where the working set exceeds 141 GB, however, the advantage diminishes, as data must traverse the PCIe 5.0 x16 interface, which, while fast, offers far less bandwidth than the on-board memory. The memory clock of 1593 MHz (6.4 Gbps effective) is a specific data point that indicates the operational frequency of the HBM3e stacks.

Power and Cooling

Power delivery is a significant consideration for this accelerator. The H200 has a TDP of 700 W, which reflects its high compute density and memory bandwidth. The suggested PSU for a system incorporating this card is 1100 W, ensuring that the rest of the system has adequate headroom. The power connector required is an 8-pin EPS, a standard for server components, rather than the 12VHPWR connector common on consumer cards.

The physical design is an SXM Module, which means it is not a standard PCIe slot card. It is designed to be mounted into a server chassis with a specialized socket and accompanying cooling solution. The 700 W TDP necessitates robust thermal management, typically in the form of a liquid-cooled loop or a high-airflow server heatsink. The absence of a slot width specification further indicates that it is not intended for tower cases. For system integrators, the 1100 W PSU recommendation is a clear guideline, and the 8-pin EPS connector must be available from the power supply. The data does not provide details on the physical dimensions, but the SXM form factor implies a specific mounting mechanism that is incompatible with consumer motherboards.

How It Compares

The nearestRivals field is empty in the data, meaning there are no direct comparison points provided for this specific accelerator in the database. Therefore, a positional analysis against named competitors cannot be derived from the given facts. The percentileVsAllGpus value of 50 indicates that it sits exactly at the median of all GPUs in the benchmark database, which includes a wide range of consumer and professional parts. This is a curious placement, as the H200’s raw memory capacity and bandwidth are exceptional, but its compute throughput (66.91 TFLOPS FP32) is not at the top of the charts when compared to the highest-end consumer cards that may have higher clock speeds.

Without rival data, the only quantitative comparison available is the percentile ranking. A 50th percentile score suggests that half of the GPUs in the database outperform it in the average benchmark score, while half underperform. This is likely skewed by the inclusion of many gaming cards that excel in rasterization benchmarks, whereas the H200 is not optimized for such tasks. In compute-specific workloads, its FP16 performance of 267.6 TFLOPS (4:1) would place it ahead of many consumer parts, but the database’s average benchmark score is not broken down by workload type in the provided information. The lack of a launch MSRP further complicates any value assessment, but that is outside the scope of this analysis.

Ray Tracing and Feature Set

The H200 does not list any dedicated ray tracing cores in its specifications, which is consistent with its server-focused design. The absence of rtCores data suggests that real-time ray tracing is not a target workload for this accelerator. Instead, the feature set is centered on AI and compute acceleration. The tensor cores are a key component, with 528 tensor cores present. These are designed to accelerate matrix operations common in deep learning, and the FP16 throughput of 267.6 TFLOPS (4:1) is a direct indicator of their capability for AI inference and training.

The API support is notably absent, with no DirectX, OpenGL, or Vulkan versions listed. This confirms that the H200 is not intended for graphics rendering in the traditional sense. The texture rate of 1,045.4 GTexel/s and pixel rate of 47.52 GPixel/s are present, but they are likely vestigial from the GH100 chip design rather than functional for display purposes. The card’s feature set is thus defined by its compute capabilities: FP32 at 66.91 TFLOPS and FP16 at 267.6 TFLOPS. The memory bandwidth of 4.89 TB/s is the enabling factor for these compute units, allowing for high utilization in memory-bound workloads. The PCIe 5.0 x16 interface is the sole external connection, and with no display outputs, the card is entirely reliant on the host system for management and data transfer.