NVIDIA H20 vs NVIDIA Jetson Orin Nano Super Comparison

FAQ

Q: What are the fundamental architectural differences between the NVIDIA H20 and the Jetson Orin Nano Super?

A: The H20 uses the GH100 chip on the Hopper architecture, built on a 5 nm process at TSMC, while the Jetson Orin Nano Super uses the GA10B chip on the Ampere architecture, built on an 8 nm process at Samsung. The H20 is a server-grade SXM module, whereas the Orin Nano Super is an IGP (integrated graphics processor) for portable devices.

Q: How do the memory subsystems compare?

A: The H20 has 96 GB of HBM3 memory on a 6144-bit bus with 4.03 TB/s bandwidth, while the Orin Nano Super has 8 GB of LPDDR5 memory on a 128-bit bus with 102.4 GB/s bandwidth. The H20's memory bandwidth is approximately 39 times higher, and its bus width is 48 times wider.

Q: Which GPU delivers higher FP32 compute throughput?

A: The H20 delivers 39.54 TFLOPS FP32, which is about 18.9 times the 2.089 TFLOPS of the Orin Nano Super. For FP16, the H20 reaches 79.07 TFLOPS versus 4.178 TFLOPS for the Orin Nano Super, a roughly 18.9-fold advantage as well.

Q: What are the power requirements?

A: The H20 has a TDP of 500 W and requires a suggested PSU of 900 W. The Orin Nano Super has a TDP of 25 W, which is 20 times lower, and does not list a suggested PSU. The H20 uses an SXM module slot, while the Orin Nano Super is an IGP.

Q: What are the physical dimensions of each product?

A: The Orin Nano Super measures 70 mm by 45 mm (2.8 inches by 1.8 inches). The H20 has no listed length, height, or width in the database. The Orin Nano Super is designed for portable form factors, while the H20 is a server module.

Q: What is the release timeline for both products?

A: The H20 was released on 2024-01-31, and the Orin Nano Super was released on 2024-12-16. Both are currently marked as Active in production status. The H20's predecessor is Server Ada, and its successor is Server Blackwell; the Orin Nano Super has no listed predecessor or successor.

Architecture Differences

The NVIDIA H20 and the NVIDIA Jetson Orin Nano Super are built on completely different architectures and process nodes. The H20 uses the GH100 chip on the Hopper architecture, fabricated on a 5 nm process at TSMC, while the Orin Nano Super uses the GA10B chip on the Ampere architecture, fabricated on an 8 nm process at Samsung. This process node difference directly impacts transistor density: the H20 packs 80,000 million transistors into an 814 mm² die, yielding a density of 98.3 million transistors per mm². The Orin Nano Super has an unknown transistor count on a 200 mm² die, with no density listed.

The H20 is a server-class part in an SXM Module slot, using PCIe 5.0 x16 for connectivity. The Orin Nano Super is an IGP for portable devices, using PCIe 4.0 x4. The H20 has no display outputs, while the Orin Nano Super's display outputs are listed as "Portable Device Dependent". API support also differs significantly: the H20 has no DirectX, OpenGL, or Vulkan support (listed as N/A), whereas the Orin Nano Super supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4.

The shading resources are vastly different. The H20 has 9,984 shading units, 312 TMUs, 24 ROPs, and 312 tensor cores. The Orin Nano Super has 1,024 shading units, 32 TMUs, 16 ROPs, and 32 tensor cores. The H20's texture rate is 617.8 GTexel/s versus 32.64 GTexel/s for the Orin Nano Super, and its pixel rate is 47.52 GPixel/s versus 16.32 GPixel/s. The H20 has a 500 W TDP and a suggested PSU of 900 W, while the Orin Nano Super has a 25 W TDP, making it far more power-efficient per watt for compute tasks, though the H20 provides far more absolute performance.

Clock speeds also differ. The H20 has a base clock of 1830 MHz and a boost clock of 1980 MHz, with memory clocked at 1313 MHz (5.3 Gbps effective). The Orin Nano Super has no base or boost clock listed, and its memory runs at 800 MHz (6.4 Gbps effective). The H20's memory interface is 6144 bits wide, versus 128 bits for the Orin Nano Super, which explains the massive bandwidth gap.

Head-to-Head Benchmarks

The database records no head-to-head benchmark entries between these two products, and neither has any individual benchmark scores or average benchmark scores. The percentileVsAllGpus field is 50 for both, indicating they sit at the median of all GPUs in the database. However, the raw specification data provides a clear quantitative comparison for compute workloads.

In FP32 compute, the H20 delivers 39.54 TFLOPS, which is 18.9 times the 2.089 TFLOPS of the Orin Nano Super. In FP16 compute, the H20 reaches 79.07 TFLOPS versus 4.178 TFLOPS, again an 18.9-fold difference. This ratio is identical because both products list their FP16 as 2:1 relative to FP32.

Memory bandwidth is the most dramatic differentiator. The H20's 4.03 TB/s is roughly 39.4 times the 102.4 GB/s of the Orin Nano Super. This bandwidth advantage is critical for large models or data-intensive workloads, as the H20 can feed its compute units far faster. The H20's 96 GB capacity is 12 times the 8 GB of the Orin Nano Super, which directly limits the size of datasets or models each can handle.

Texture rate favors the H20 at 617.8 GTexel/s, which is 18.9 times the 32.64 GTexel/s of the Orin Nano Super. Pixel rate is closer but still heavily skewed: the H20's 47.52 GPixel/s is 2.9 times the 16.32 GPixel/s of the Orin Nano Super. The H20's ROP count of 24 is only 1.5 times the Orin Nano Super's 16, which explains the smaller pixel rate margin.

The H20's tensor core count is 312 versus 32 for the Orin Nano Super, a 9.75-fold difference. This suggests the H20 is far better suited for AI inference and training workloads that rely on tensor operations, though the Orin Nano Super's 32 tensor cores still provide meaningful acceleration for its class.

Neither product has any recorded benchmark wins in the headToHeadBenchmarks array, and the winsA and winsB fields are both 0. The analysis therefore relies entirely on the specification-based deltas described above.

The Verdict

The data indicates that the NVIDIA H20 is the clear choice for high-throughput server workloads, while the Jetson Orin Nano Super is designed for edge or portable applications with strict power and size constraints. The H20's 39.54 TFLOPS FP32 and 4.03 TB/s bandwidth are roughly 19 times and 39 times higher than the Orin Nano Super's respective figures. Any workload that requires large memory capacity (96 GB versus 8 GB) or sustained high compute throughput will favor the H20.

The Orin Nano Super wins on power efficiency and physical footprint. Its 25 W TDP is 20 times lower than the H20's 500 W, and its 70 mm by 45 mm dimensions make it suitable for embedded systems. The H20 requires an SXM Module slot and a 900 W suggested PSU, which confines it to data center installations. The Orin Nano Super also supports display outputs and a full API stack (DirectX 12 Ultimate, OpenGL 4.6, Vulkan 1.4), while the H20 has no display outputs and no API support, rendering it unusable for graphics-oriented tasks.

The launch MSRP of the Orin Nano Super is 249 USD, while the H20 has no listed launch MSRP. The H20 is a server part with no pricing data, but its specification profile places it in the enterprise segment. The Orin Nano Super's production status is Active, and it was released on 2024-12-16, which is later than the H20's 2024-01-31 release.

Users who need to run large language models, high-resolution inference, or massive parallel compute should select the H20. Users who need a compact, low-power device for real-time inference at the edge, or who require DirectX or Vulkan support, should select the Orin Nano Super. The data does not show any scenario where the Orin Nano Super outperforms the H20 in raw compute or bandwidth, but it clearly outperforms the H20 in portability and power efficiency.

Specification Differences

The following fields differ between the NVIDIA H20 and the NVIDIA Jetson Orin Nano Super:

• Chip: GH100 versus GA10B
• Architecture: Hopper versus Ampere
• Generation: Server Hopper (Hxx) versus Tegra (Ampere)
• Process Node: 5 nm versus 8 nm
• Foundry: TSMC versus Samsung
• Transistors: 80,000 million versus unknown
• Die Size: 814 mm² versus 200 mm²
• Transistor Density: 98.3M / mm² versus null
• Base Clock: 1830 MHz versus null
• Boost Clock: 1980 MHz versus null
• Memory Clock: 1313 MHz 5.3 Gbps effective versus 800 MHz 6.4 Gbps effective
• Memory Size: 96 GB versus 8 GB
• Memory Type: HBM3 versus LPDDR5
• Memory Bus Width: 6144 bit versus 128 bit
• Memory Bandwidth: 4.03 TB/s versus 102.4 GB/s
• Shading Units: 9984 versus 1024
• TMUs: 312 versus 32
• ROPs: 24 versus 16
• Tensor Cores: 312 versus 32
• Pixel Rate: 47.52 GPixel/s versus 16.32 GPixel/s
• Texture Rate: 617.8 GTexel/s versus 32.64 GTexel/s
• FP32: 39.54 TFLOPS versus 2.089 TFLOPS
• FP16: 79.07 TFLOPS versus 4.178 TFLOPS
• TDP: 500 W versus 25 W
• Slot Width: SXM Module versus IGP
• Suggested PSU: 900 W versus null
• Bus Interface: PCIe 5.0 x16 versus PCIe 4.0 x4
• Display Outputs: No outputs versus Portable Device Dependent
• DirectX: N/A versus 12 Ultimate (12_2)
• OpenGL: N/A versus 4.6
• Vulkan: N/A versus 1.4
• Dimensions (length): null versus 70 mm 2.8 inches
• Dimensions (height): null versus 45 mm 1.8 inches
• Release Date: 2024-01-31 versus 2024-12-16
• Predecessor: Server Ada versus null
• Successor: Server Blackwell versus null
• Launch MSRP: null versus 249 USD

Fields that do not differ: manufacturer (NVIDIA for both), production status (Active for both), and percentileVsAllGpus (50 for both). The H20 has no listed power connectors, and neither does the Orin Nano Super.

Where Each One Wins

The H20 wins decisively in absolute compute performance. Its FP32 throughput of 39.54 TFLOPS is 18.9 times higher than the Orin Nano Super's 2.089 TFLOPS, and its FP16 throughput of 79.07 TFLOPS is likewise 18.9 times higher. For any workload that is compute-bound, such as large-scale matrix multiplication, scientific simulation, or batch AI inference, the H20 is the only viable option based on the recorded data.

The H20 also wins in memory capacity and bandwidth. With 96 GB of HBM3 and 4.03 TB/s bandwidth, it can hold and process datasets that are 12 times larger than the Orin Nano Super's 8 GB LPDDR5, and it can move data through the system roughly 39 times faster. This makes the H20 superior for training or inference on large language models, high-resolution video processing, or any workload where memory access is the bottleneck.

The H20 wins in texture throughput, with 617.8 GTexel/s versus 32.64 GTexel/s, and in pixel rate, with 47.52 GPixel/s versus 16.32 GPixel/s. The H20 also has 9,984 shading units versus 1,024, and 312 tensor cores versus 32. These advantages make the H20 the clear winner for rendering, deep learning, and general-purpose GPU compute.

The Orin Nano Super wins in power efficiency and form factor. Its 25 W TDP is 20 times lower than the H20's 500 W, allowing it to operate in battery-powered or passively cooled systems. Its dimensions of 70 mm by 45 mm make it suitable for compact embedded devices, drones, robotics, or portable AI appliances. The H20's SXM Module slot and 900 W suggested PSU require a server chassis with robust power delivery, which is not portable.

The Orin Nano Super also wins in software and display support. It supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the H20 has N/A for all three APIs. The Orin Nano Super's display outputs are listed as "Portable Device Dependent", meaning it can drive screens in compatible devices, whereas the H20 has no display outputs at all. For graphics, gaming, or interactive edge applications, the Orin Nano Super is the only product with the necessary API support.

The Orin Nano Super wins on release timing for those who need the latest edge platform, having been released on 2024-12-16 versus the H20's 2024-01-31. However, the H20's position in the Server Hopper generation and its successor Server Blackwell indicate a longer product roadmap in the enterprise segment. The Orin Nano Super has no listed predecessor or successor, suggesting a standalone product line.

In summary, the H20 is the winner for data center compute, large memory workloads, and maximum throughput. The Orin Nano Super is the winner for edge inference, portable devices, graphics output, and low-power operation. The recorded data does not support any other use-case split.