NVIDIA H100 SXM5 96 GB

How It Compares

The NVIDIA H100 SXM5 96 GB occupies a unique position in the database: it is a server-grade accelerator with no direct rivals listed in its nearestRivals field. This absence of comparative data means the H100 must be evaluated on its absolute specifications and architectural merits rather than through head-to-head percentage deltas. The data shows a GPU designed for a specific computational niche, not for general competitive benchmarking.

Without nearestRival entries, the H100’s percentileVsAllGpus score of 50 serves as the sole positional reference. This percentile indicates that, within the entire database of GPUs, the H100 sits at the median point — a result that reflects the database’s inclusion of both consumer and server parts. The avgBenchmarkScore of 0 further reinforces that this unit has no standardized benchmark results recorded, making the percentile a structural artifact of the database rather than a performance verdict.

The absence of rivals is itself informative. The H100 SXM5 is not positioned against consumer or workstation GPUs; it is a compute-focused module with a 96 GB HBM3 memory configuration and a 5120-bit bus. In the context of the database, this product exists as a standalone entry, defined by its raw specifications — 16,896 shading units, 528 tensor cores, and a 700 W TDP — rather than by competitive positioning.

Ray Tracing and Feature Set

The H100 SXM5 does not include dedicated ray tracing cores; the rtCores field is null. This is consistent with its server-oriented Hopper architecture, which prioritizes tensor operations over graphics-centric workloads. The GPU’s 528 tensor cores are the primary compute engines, supporting the 267.6 TFLOPS fp16 performance (at a 4:1 ratio) that dominates its feature set.

API support is notably absent: the directx, opengl, and vulkan fields are all null. This indicates the H100 is not intended for real-time graphics rendering or traditional gaming workloads. The displayOutputs field is listed as "No outputs," confirming that this is a compute module without video signal capabilities. It is designed for data centers, scientific computing, and AI training — environments where rendering pipelines are irrelevant.

The architecture is Hopper, manufactured on a 5 nm process at TSMC. The GH100 chip contains 80,000 million transistors on an 814 mm² die, yielding a transistor density of 98.3M per mm². This density figure places the H100 among the most complex chips in the database. The memory subsystem uses HBM3 with 5.3 Gbps effective speed, delivering 3.36 TB/s of bandwidth across a 5120-bit interface — a configuration built for massive parallel data movement rather than pixel output.

Benchmark Performance

The benchmark section for the H100 SXM5 is empty, with no scores recorded. The avgBenchmarkScore is 0, and the nearestRivals array is empty. This means the database contains no synthetic or real-world test results for this specific SKU. The percentileVsAllGpus value of 50 is the only quantitative performance indicator, and it should be interpreted cautiously: it reflects the GPU’s position in a database that includes consumer cards, but without benchmark data, this percentile cannot be cross-referenced against actual workload performance.

Given the absence of rival deltas, the analysis must rely on theoretical specifications. The fp32 compute rate is 66.91 TFLOPS, which is the standard metric for general-purpose compute. The fp16 rate of 267.6 TFLOPS (4:1) is four times higher, indicating a design heavily optimized for mixed-precision tensor workloads. The texture rate is 1,045.4 GTexel/s, and the pixel rate is 47.52 GPixel/s — figures that are high in absolute terms but irrelevant for a card with no display outputs.

The memory bandwidth of 3.36 TB/s is the standout specification. In the absence of competitor data, this figure can be contextualized by the memory configuration: 96 GB of HBM3 across a 5120-bit bus, running at 1313 MHz (5.3 Gbps effective). This bandwidth is essential for feeding the 528 tensor cores, which require sustained data throughput to maintain their fp16 peak. The practical implication is that the H100’s performance in AI training or inference tasks is likely bounded by memory capacity and bandwidth rather than core count — a characteristic that differentiates it from graphics-first GPUs.

Power and Cooling

The H100 SXM5 has a TDP of 700 W, a figure that places it in the highest power-consumption tier of the database. This is a direct consequence of the dense transistor count (80,000 million on 814 mm²) and the high clock speeds: base 1350 MHz and boost 1980 MHz. The boost clock indicates the module can sustain significant frequency headroom under load, provided cooling and power delivery are adequate.

The suggested PSU is 1100 W, which is the recommended system power supply when integrating this module. This is not a card-level power requirement but a system-level recommendation, accounting for the CPU, memory, and other components. The power connector is specified as 8-pin EPS, which is a server-oriented connector rather than the PCIe 8-pin or 12VHPWR connectors common on consumer GPUs. This reinforces the H100’s data-center design.

Cooling is handled via the SXM Module form factor, which is a slot-width specification rather than a traditional PCIe card. The slotWidth field lists "SXM Module," indicating the H100 is designed for NVIDIA’s proprietary SXM socket, which typically uses chassis-level cooling solutions — either passive heatsinks with server airflow or liquid cooling systems. The bus interface is PCIe 5.0 x16, which provides the host connection for data transfer, though the module itself does not rely on slot cooling. The absence of display outputs means no video BIOS or output stage, further reducing cooling complexity compared to graphics cards.

FAQ

Q: What is the memory configuration of the H100 SXM5?

A: The H100 SXM5 features 96 GB of HBM3 memory on a 5120-bit bus, with a bandwidth of 3.36 TB/s. The memory clock is 1313 MHz, resulting in 5.3 Gbps effective speed.

Q: Does the H100 support ray tracing?

A: No. The rtCores field is null, and the API support for DirectX, OpenGL, and Vulkan is also null. The H100 has no display outputs and is designed exclusively for compute workloads, not graphics rendering.

Q: What is the power requirement for this GPU?

A: The TDP is 700 W, and the suggested PSU is 1100 W. The power connector is an 8-pin EPS, which is a server-grade connector. The slot width is SXM Module, meaning it uses NVIDIA’s proprietary socket with chassis-level cooling.

Q: What is the compute performance of the H100 in fp32 and fp16?

A: The fp32 performance is 66.91 TFLOPS, while the fp16 performance is 267.6 TFLOPS at a 4:1 ratio. The fp16 figure is four times higher, reflecting the tensor core optimization for AI workloads.

Q: What is the manufacturing process and chip size?

A: The H100 uses a 5 nm process from TSMC. The GH100 chip contains 80,000 million transistors on an 814 mm² die, with a transistor density of 98.3M per mm².

Q: Why is the benchmark score zero?

A: The avgBenchmarkScore is 0 because no benchmark results are recorded for this SKU in the database. The percentileVsAllGpus is 50, which indicates the median position among all GPUs, but this should not be interpreted as a performance metric without actual test data.

Who Should Consider It

The H100 SXM5 is a compute module, not a graphics card, and its target user is defined by the absence of display outputs and graphics APIs. The 96 GB HBM3 memory and 3.36 TB/s bandwidth make it suitable for workloads that require large model residency and high-throughput data movement — typical of large-scale AI training, scientific simulation, and data analytics. The fp16 performance of 267.6 TFLOPS suggests a strong fit for mixed-precision tensor operations, which dominate modern deep learning frameworks.

The 700 W TDP and 1100 W suggested PSU indicate this is not a consumer product. Users must have a server infrastructure capable of delivering 8-pin EPS power and managing the thermal output of the SXM Module form factor. The PCIe 5.0 x16 bus interface ensures high-speed host communication, but the card’s primary bottleneck will be the software stack and memory capacity, not interface bandwidth.

For resolution and settings-based recommendations, the data provides no gaming or real-time graphics scores, so this section must be interpreted in compute terms. The H100 is not suitable for any resolution-based gaming workload — it has no outputs and no graphics API support. Instead, the relevant "resolution" is the model size or dataset size that fits within 96 GB. Users with workloads exceeding 96 GB will need to consider multi-GPU configurations, while those within that limit can leverage the 3.36 TB/s bandwidth to minimize data transfer stalls.

The percentileVsAllGpus of 50 is a neutral indicator; it neither recommends nor discourages the purchase. The productionStatus is "Active," meaning the H100 is currently available. The release date is March 20, 2023, with a predecessor of Server Ada and a successor of Server Blackwell — indicating this is a mid-generation server product. The absence of a launch MSRP in the data means no pricing information is available for analysis.

In summary, the H100 SXM5 is for organizations that need a high-capacity, high-bandwidth compute accelerator with no graphics aspirations. Its 528 tensor cores and 267.6 TFLOPS fp16 rate are the primary value proposition, supported by a 96 GB HBM3 pool. The lack of benchmark data and rivals in this database limits comparative analysis, but the raw specifications paint a clear picture: this is a specialized tool for a specific computational niche, and its suitability depends entirely on whether the workload matches its memory and tensor capabilities. For anyone requiring a general-purpose GPU for rendering or consumer workloads, the H100 is fundamentally mismatched. For those running large-scale AI models that fit in 96 GB, the H100’s specifications suggest it can handle the task, though the database provides no empirical scores to validate that expectation.