NVIDIA H100 SXM5 64 GB vs NVIDIA Rubin GPU Comparison

NVIDIA’s server GPU lineup spans two distinct architectures in the H100 SXM5 64 GB and the Rubin GPU. The H100 is a Hopper-generation part built for the current wave of AI training and inference, while the Rubin GPU is a next-generation Rubin-architecture design with a substantially larger memory footprint and higher peak throughput. The database records no direct head-to-head benchmark scores for these two parts, so the analysis below relies on the recorded specifications, architectural details, and the relative positioning implied by the data. The H100 SXM5 64 GB sits at the 50th percentile among all GPUs in the database, and the Rubin GPU also sits at the 50th percentile, indicating that neither part has accumulated a meaningful set of benchmark results to differentiate them statistically. Both are active production parts with no launch MSRP recorded.

Head-to-Head Benchmarks

The database contains no direct benchmark scores for either GPU, and the head-to-head benchmark table is empty. This means the comparison must be derived from the recorded performance metrics rather than actual test results. The most striking difference appears in memory bandwidth. The H100 SXM5 64 GB delivers 2.02 TB/s across a 3072-bit HBM3 interface, while the Rubin GPU provides 22.1 TB/s across a 16384-bit HBM4 bus. The Rubin GPU’s bandwidth is roughly ten times higher, a gap that directly impacts memory-bound workloads such as large-scale transformer training or batch inference with long contexts. The H100’s memory interface is 3072 bits wide, whereas the Rubin GPU’s is 16384 bits, and the memory types differ: HBM3 for the H100, HBM4 for the Rubin.

Raw compute throughput also favors the Rubin GPU. The H100 SXM5 64 GB achieves 66.91 TFLOPS FP32 and 267.6 TFLOPS FP16 with a 4:1 ratio. The Rubin GPU reaches 130.0 TFLOPS FP32 and 260.0 TFLOPS FP16 with a 2:1 ratio. In FP32, the Rubin GPU is approximately 1.94 times faster than the H100. In FP16, the two parts are nearly tied: the H100’s 267.6 TFLOPS is marginally higher than the Rubin GPU’s 260.0 TFLOPS, a difference of about 2.8%. This near parity in FP16 is notable because many AI workloads use FP16 or mixed precision. The H100 achieves its FP16 number through a 4:1 ratio, while the Rubin GPU uses a 2:1 ratio, meaning the Rubin GPU’s FP16 performance is derived from its FP32 units more directly.

Pixel and texture rates show a similar pattern. The H100 has a pixel rate of 47.52 GPixel/s and a texture rate of 1,045.4 GTexel/s. The Rubin GPU delivers 54.41 GPixel/s and 2,031.2 GTexel/s. The texture rate more than doubles, reflecting the Rubin GPU’s higher TMU count (896 vs. 528). The pixel rate advantage is smaller, only about 14.5%, because both parts have the same number of ROPs: 24. The Rubin GPU’s shading unit count is 28,672 versus the H100’s 16,896, a 70% increase that explains the FP32 lead.

Clock speeds complicate the comparison. The H100 has a base clock of 1665 MHz and a boost clock of 1980 MHz. The Rubin GPU has a much lower base clock of 700 MHz but a higher boost clock of 2267 MHz. The Rubin GPU’s boost clock is 14.5% higher than the H100’s, which helps it overcome the lower base frequency. The H100’s memory clock is listed at 1313 MHz with 5.3 Gbps effective, while the Rubin GPU’s memory clock is 2695 MHz with 10.8 Gbps effective. The effective memory speed doubles, contributing to the bandwidth increase.

The transistor counts and die sizes are dramatically different. The H100 uses 80,000 million transistors on an 814 mm² die, yielding a density of 98.3M per mm². The Rubin GPU packs 336,000 million transistors on a 1456 mm² die, a density of 230.8M per mm². The Rubin GPU has 4.2 times the transistor count and a die that is 79% larger. The process node differs as well: the H100 uses a 5 nm process from TSMC, while the Rubin GPU uses a 3 nm process from the same foundry. This explains the higher transistor density despite the larger absolute die.

Where Each One Wins

The H100 SXM5 64 GB wins in one narrow but important metric: FP16 peak throughput. The recorded data shows 267.6 TFLOPS for the H100 versus 260.0 TFLOPS for the Rubin GPU. For applications that rely on FP16 tensor operations and cannot use FP32 or FP8 paths, the H100 holds a small edge. The H100 also has a higher base clock (1665 MHz vs. 700 MHz), which could matter for workloads that run at base frequency for sustained periods, though the Rubin GPU’s boost clock is higher. The H100’s memory bandwidth of 2.02 TB/s is lower, but the H100’s 64 GB capacity may be sufficient for models that fit within that footprint, and the smaller memory size can reduce the memory access latency for certain access patterns.

The Rubin GPU wins decisively in memory capacity and bandwidth. With 288 GB of HBM4 and 22.1 TB/s of bandwidth, it can hold far larger models and move data much faster. The FP32 throughput is 130.0 TFLOPS, nearly double the H100’s 66.91 TFLOPS. The texture rate of 2,031.2 GTexel/s is 94% higher than the H100’s 1,045.4 GTexel/s. The pixel rate of 54.41 GPixel/s is 14.5% higher. The Rubin GPU’s shading unit count of 28,672 versus 16,896 gives it a 70% advantage in parallel integer and FP32 work.

For memory-bound workloads, the Rubin GPU’s 22.1 TB/s bandwidth is the defining advantage. The H100’s 2.02 TB/s is a fraction of that. For compute-bound FP16 workloads, the H100’s 267.6 TFLOPS is slightly ahead, but the Rubin GPU’s 260.0 TFLOPS is close enough that other factors, such as memory bandwidth and capacity, dominate. The Rubin GPU’s transistor count of 336,000 million versus 80,000 million suggests a much larger compute complex, which is consistent with its higher shading unit and tensor core counts.

Architecture Differences

The H100 SXM5 64 GB uses the GH100 chip with the Hopper architecture, belonging to the Server Hopper (Hxx) generation. The Rubin GPU uses the GR100 chip with the Rubin architecture, part of the Server Rubin (Rxx) generation. These are separate design families. The process nodes differ: the H100 is fabricated on a 5 nm process at TSMC, while the Rubin GPU uses a 3 nm process at TSMC. The die sizes are 814 mm² for the H100 and 1456 mm² for the Rubin GPU, with transistor counts of 80,000 million and 336,000 million respectively. The transistor density increases from 98.3M per mm² to 230.8M per mm², reflecting the smaller process node.

The memory subsystems are architecturally distinct. The H100 uses HBM3 with a 3072-bit bus and 2.02 TB/s bandwidth. The Rubin GPU uses HBM4 with a 16384-bit bus and 22.1 TB/s bandwidth. The bus width is more than five times wider, and the memory type is a generation newer. The memory clock is also higher: 1313 MHz for the H100 versus 2695 MHz for the Rubin GPU. Both parts have no display outputs, consistent with server accelerator roles.

The compute resources differ in scale. The H100 has 16,896 shading units, 528 TMUs, 24 ROPs, and 528 tensor cores. The Rubin GPU has 28,672 shading units, 896 TMUs, 24 ROPs, and 896 tensor cores. The ROP count is identical, which explains the modest pixel rate difference. The tensor core count doubles from 528 to 896. Neither part has recorded RT cores. The API support is absent for the H100 (no DirectX, OpenGL, or Vulkan listed), while the Rubin GPU lists N/A for all three APIs, indicating neither is intended for graphics workloads.

The bus interfaces also differ. The H100 uses PCIe 5.0 x16, while the Rubin GPU uses PCIe 6.0 x16. The power delivery differs: the H100 has an 8-pin EPS connector, while the Rubin GPU lists no power connector details. The suggested PSU is 1100 W for the H100 and 2700 W for the Rubin GPU, reflecting the latter’s higher power draw. The H100’s TDP is 700 W, while the Rubin GPU’s TDP is 2300 W, a difference of 1600 W.

Specification Differences

The two GPUs differ across nearly every specification category. The process node is 5 nm for the H100 and 3 nm for the Rubin GPU. The transistor count is 80,000 million versus 336,000 million. The die size is 814 mm² versus 1456 mm². The base clock is 1665 MHz versus 700 MHz. The boost clock is 1980 MHz versus 2267 MHz. The memory clock is 1313 MHz (5.3 Gbps effective) versus 2695 MHz (10.8 Gbps effective).

Memory size is 64 GB versus 288 GB. Memory type is HBM3 versus HBM4. Bus width is 3072 bit versus 16384 bit. Bandwidth is 2.02 TB/s versus 22.1 TB/s. Shading units are 16,896 versus 28,672. TMUs are 528 versus 896. ROPs are equal at 24. Tensor cores are 528 versus 896. Pixel rate is 47.52 GPixel/s versus 54.41 GPixel/s. Texture rate is 1,045.4 GTexel/s versus 2,031.2 GTexel/s. FP32 is 66.91 TFLOPS versus 130.0 TFLOPS. FP16 is 267.6 TFLOPS (4:1) versus 260.0 TFLOPS (2:1).

TDP is 700 W versus 2300 W. The power connector is 8-pin EPS for the H100, while the Rubin GPU has no recorded connector. The suggested PSU is 1100 W versus 2700 W. The bus interface is PCIe 5.0 x16 versus PCIe 6.0 x16. Both use an SXM Module slot. The release dates differ: the H100 launched on 2023-03-20, while the Rubin GPU is dated 2025-12-31. The H100’s predecessor is Server Ada and its successor is Server Blackwell. The Rubin GPU’s predecessor is Server Blackwell, with no successor recorded.

FAQ

Q: Which GPU has higher FP32 throughput?

A: The Rubin GPU has 130.0 TFLOPS FP32, which is about 1.94 times the H100’s 66.91 TFLOPS.

Q: How do the memory bandwidths compare?

A: The H100 provides 2.02 TB/s over HBM3, while the Rubin GPU provides 22.1 TB/s over HBM4, a difference of roughly 10.9 times.

Q: Which GPU has more memory?

A: The Rubin GPU has 288 GB of HBM4, compared to 64 GB of HBM3 on the H100.

Q: What is the transistor count difference?

A: The H100 has 80,000 million transistors on an 814 mm² die, while the Rubin GPU has 336,000 million transistors on a 1456 mm² die.

Q: Are the FP16 performances similar?

A: Yes, the H100 achieves 267.6 TFLOPS FP16 (4:1), and the Rubin GPU achieves 260.0 TFLOPS FP16 (2:1), a difference of about 2.8% in favor of the H100.

Q: What are the power requirements?

A: The H100 has a TDP of 700 W with a suggested PSU of 1100 W. The Rubin GPU has a TDP of 2300 W with a suggested PSU of 2700 W.

The Verdict

The data points to a clear split. The H100 SXM5 64 GB is the choice for workloads that are constrained by FP16 peak throughput, where it posts 267.6 TFLOPS versus the Rubin GPU’s 260.0 TFLOPS. The H100 also has a higher base clock of 1665 MHz versus 700 MHz, which may benefit applications that run at base frequency for long durations. Its 64 GB HBM3 memory and 2.02 TB/s bandwidth are adequate for models that fit within that capacity, and its 700 W TDP makes it a more tractable part for systems with lower power budgets. The suggested PSU of 1100 W is a fraction of the Rubin GPU’s 2700 W requirement.

The Rubin GPU is the stronger part for memory-intensive and FP32-heavy workloads. Its 288 GB HBM4 memory and 22.1 TB/s bandwidth represent a massive scaling advantage for large models, and its FP32 throughput of 130.0 TFLOPS is nearly double the H100’s. The texture rate of 2,031.2 GTexel/s is 94% higher. The Rubin GPU’s 3 nm process and 336,000 million transistors indicate a much larger compute complex, and its PCIe 6.0 x16 interface is a generation ahead. The 2300 W TDP and 2700 W suggested PSU are significant system requirements, but the performance deltas justify the power draw for high-end training clusters.

For FP16 mixed-precision training, the H100’s 267.6 TFLOPS is marginally ahead, but the Rubin GPU’s 260.0 TFLOPS is close enough that memory bandwidth becomes the limiting factor. Since the Rubin GPU offers 22.1 TB/s versus 2.02 TB/s, it will likely outperform the H100 in practice for large batch sizes. The H100 wins only in the narrow FP16 peak number and the base clock. The Rubin GPU wins in every other recorded performance metric except FP16, where the H100 has a 7.6 TFLOPS edge. The verdict from the data is that the H100 suits existing Hopper deployments with moderate memory needs, while the Rubin GPU is the higher-capability part for next-generation models that require 288 GB of memory and 22.1 TB/s of bandwidth.