NVIDIA H100 CNX vs NVIDIA H20 Comparison

Head-to-Head Benchmarks

The database contains no recorded benchmark scores for either the NVIDIA H100 CNX or the NVIDIA H20. Both entries show an average benchmark score of 0, and the head-to-head benchmark comparison table is empty. Consequently, there are no measured performance deltas to report between these two accelerators. The percentile rank for both parts sits at 50, which places them at the median of the database's GPU distribution, but this percentile is derived from an empty benchmark set and should not be interpreted as a performance advantage for either product.

Without recorded benchmark data, the comparison must rely entirely on the specification sheets. The H100 CNX presents a higher FP32 throughput at 53.84 TFLOPS versus the H20's 39.54 TFLOPS, representing a 36% advantage in raw single-precision compute on paper. The H100 CNX also leads in FP16 performance with 215.4 TFLOPS (4:1) compared to the H20's 79.07 TFLOPS (2:1), a 2.7x gap in half-precision throughput. These figures suggest the H100 CNX holds a substantial theoretical edge in compute-heavy workloads, but without actual benchmark scores, the real-world impact remains unverified.

The H20 counters with a larger memory pool and faster memory subsystem. It carries 96 GB of HBM3 across a 6144-bit bus, yielding 4.03 TB/s of bandwidth. The H100 CNX uses 80 GB of HBM2e on a 5120-bit bus, delivering 2.04 TB/s. The H20's memory bandwidth is nearly double that of the H100 CNX, a difference that could favor memory-bound operations such as large model inference or data-intensive graph analytics. The H20 also runs higher clocks, with a base of 1830 MHz and a boost of 1980 MHz, versus the H100 CNX's 690 MHz base and 1845 MHz boost.

FAQ

Q: Which GPU has more shading units?

A: The NVIDIA H100 CNX has 14,592 shading units, while the NVIDIA H20 has 9,984. The H100 CNX also carries 456 TMUs and 456 tensor cores, compared to the H20's 312 TMUs and 312 tensor cores.

Q: What are the memory specifications for each card?

A: The H100 CNX uses 80 GB of HBM2e memory with a 5120-bit bus and 2.04 TB/s bandwidth. The H20 uses 96 GB of HBM3 memory with a 6144-bit bus and 4.03 TB/s bandwidth.

Q: Are both GPUs based on the same chip?

A: Yes, both the H100 CNX and the H20 use the GH100 chip from NVIDIA's Hopper architecture. Both are manufactured by TSMC on a 5 nm process node, with 80,000 million transistors and a die size of 814 mm².

Q: How do their power requirements differ?

A: The H100 CNX has a TDP of 350 W and a suggested PSU of 750 W, using an 8-pin EPS connector. The H20 has a TDP of 500 W and a suggested PSU of 900 W, with no power connector listed because it is an SXM module.

Q: What is the form factor of each card?

A: The H100 CNX is a dual-slot card with a length of 267 mm (10.5 inches) and a height of 111 mm (4.4 inches). The H20 is an SXM module with no listed dimensions.

Q: When did each GPU launch?

A: The H100 CNX was released on March 20, 2023. The H20 was released on January 31, 2024. Both are currently listed as Active in production status.

Architecture Differences

Both accelerators share the same foundational architecture: the GH100 chip on NVIDIA's Hopper architecture, fabricated by TSMC on a 5 nm process. The transistor count is identical at 80,000 million, and the die size matches at 814 mm², yielding a transistor density of 98.3M per mm² for each. The generation label is "Server Hopper (Hxx)" for both, and neither has a codename.

The differences emerge in the execution resources and memory design. The H100 CNX allocates 14,592 shading units, 456 TMUs, and 456 tensor cores, while the H20 reduces these to 9,984 shading units, 312 TMUs, and 312 tensor cores. Both have 24 ROPs. This means the H100 CNX has 46% more shading units and 46% more tensor cores than the H20, a substantial imbalance in parallel compute capacity.

Memory architecture diverges significantly. The H100 CNX uses HBM2e with an 80 GB capacity, a 5120-bit bus, and 2.04 TB/s bandwidth. The H20 uses HBM3 with a 96 GB capacity, a 6144-bit bus, and 4.03 TB/s bandwidth. The H20's memory clock runs at 1313 MHz (5.3 Gbps effective), while the H100 CNX's memory clock is 1593 MHz (3.2 Gbps effective). Despite the lower effective data rate per pin, the wider bus on the H20 more than compensates, nearly doubling total bandwidth.

Clock speeds also differ. The H100 CNX has a base clock of 690 MHz and a boost clock of 1845 MHz. The H20 starts at a much higher base of 1830 MHz and boosts to 1980 MHz. The H20's higher base clock suggests it maintains consistent performance under sustained loads, while the H100 CNX relies on its boost behavior to reach peak throughput.

The H100 CNX is a dual-slot PCIe card with an 8-pin EPS connector, a 350 W TDP, and a suggested PSU of 750 W. The H20 is an SXM module with a 500 W TDP and a suggested PSU of 900 W, with no dedicated power connector listed because SXM modules draw power through the socket. Both use PCIe 5.0 x16 for host connectivity and have no display outputs. The H20 lists its DirectX, OpenGL, and Vulkan APIs as N/A, while the H100 CNX leaves these fields null.

The Verdict

The data indicates a clear split between compute density and memory capacity. The H100 CNX is the stronger choice for workloads that demand raw FP32 or FP16 throughput, given its higher shading unit count, tensor core count, and FP32/FP16 TFLOPS ratings. The H20 is the better option for memory-bound tasks that need the larger 96 GB pool and the 4.03 TB/s bandwidth, such as hosting very large models or processing massive datasets that exceed the H100 CNX's 80 GB capacity.

For users who prioritize peak compute performance in a dual-slot PCIe form factor with a lower 350 W TDP, the H100 CNX offers a theoretical 36% advantage in FP32 and a 2.7x advantage in FP16 on paper. For users who need the highest memory bandwidth and capacity in an SXM module with a 500 W TDP, the H20 provides nearly double the bandwidth and 16 GB more memory. Neither part has recorded benchmark scores, so the final choice rests on which specification category aligns with the intended workload.

Specification Differences

| Specification | NVIDIA H100 CNX | NVIDIA H20 |

| --- | --- | --- |

| Base Clock | 690 MHz | 1830 MHz |

| Boost Clock | 1845 MHz | 1980 MHz |

| Memory Clock | 1593 MHz (3.2 Gbps effective) | 1313 MHz (5.3 Gbps effective) |

| Memory Size | 80 GB | 96 GB |

| Memory Type | HBM2e | HBM3 |

| Memory Bus Width | 5120 bit | 6144 bit |

| Memory Bandwidth | 2.04 TB/s | 4.03 TB/s |

| Shading Units | 14592 | 9984 |

| TMUs | 456 | 312 |

| Tensor Cores | 456 | 312 |

| Pixel Rate | 44.28 GPixel/s | 47.52 GPixel/s |

| Texture Rate | 841.3 GTexel/s | 617.8 GTexel/s |

| FP32 Performance | 53.84 TFLOPS | 39.54 TFLOPS |

| FP16 Performance | 215.4 TFLOPS (4:1) | 79.07 TFLOPS (2:1) |

| TDP | 350 W | 500 W |

| Slot Width | Dual-slot | SXM Module |

| Power Connectors | 8-pin EPS | None listed |

| Suggested PSU | 750 W | 900 W |

| Dimensions | 267 mm x 111 mm | Not listed |

| Release Date | 2023-03-20 | 2024-01-31 |

Where Each One Wins

The H100 CNX wins in compute throughput metrics. Its FP32 rating of 53.84 TFLOPS exceeds the H20's 39.54 TFLOPS, and its FP16 rating of 215.4 TFLOPS dwarfs the H20's 79.07 TFLOPS. The texture rate also favors the H100 CNX at 841.3 GTexel/s versus 617.8 GTexel/s. These figures point to workloads that saturate tensor cores or shader units, such as training dense neural networks, scientific simulation, or high-resolution rendering where FP32 precision matters.

The H20 wins in memory-centric specifications. Its 96 GB capacity and 4.03 TB/s bandwidth give it a clear edge for workloads that cannot fit into 80 GB or that stream large volumes of data. The higher pixel rate of 47.52 GPixel/s versus the H100 CNX's 44.28 GPixel/s also goes to the H20, suggesting a slight advantage in fill-rate-bound operations. The H20's higher base and boost clocks (1830 MHz and 1980 MHz versus 690 MHz and 1845 MHz) indicate it may sustain higher clock speeds under load, which could benefit latency-sensitive tasks.

The H100 CNX offers a lower TDP of 350 W and a dual-slot form factor, making it easier to integrate into standard PCIe chassis with a 750 W PSU recommendation. The H20 requires an SXM socket and a 900 W PSU, which limits it to specialized server platforms. The choice between these two accelerators hinges on whether the workload is compute-bound, favoring the H100 CNX, or memory-bound, favoring the H20.