NVIDIA H20 NVL16 vs NVIDIA H800 SXM5 Comparison

Where Each One Wins

The recorded data presents an unusual comparison: neither GPU shows a benchmark win in the head-to-head dataset, as the head-to-head benchmark array is empty and both parts record zero wins. This absence of measured scores means the analysis must rely entirely on the specification-level differences to determine where each unit holds an advantage.

The NVIDIA H20 NVL16 positions itself as the higher-frequency part. Its base clock of 1830 MHz and boost clock of 1980 MHz exceed the H800 SXM5's 1095 MHz base and 1755 MHz boost. That clock advantage translates directly into pixel throughput, where the H20 NVL16 reaches 47.52 GPixel/s versus 42.12 GPixel/s for the H800 SXM5. For workloads that stress the rasterization pipeline, the H20 NVL16 delivers roughly 13% more pixel fill rate, a measurable edge.

However, the H800 SXM5 counters with a far larger execution resource pool. It carries 16896 shading units, 528 texture mapping units, and 528 tensor cores, while the H20 NVL16 has 9984 shading units, 312 TMUs, and 312 tensor cores. The H800 SXM5's texture rate of 926.6 GTexel/s is substantially higher than the H20 NVL16's 617.8 GTexel/s. In raw FP32 compute, the H800 SXM5 delivers 59.30 TFLOPS against the H20 NVL16's 39.54 TFLOPS.

The FP16 comparison favors the H800 SXM5 even more decisively. The H800 SXM5 achieves 237.2 TFLOPS with a 4:1 ratio, while the H20 NVL16 manages 79.07 TFLOPS with a 2:1 ratio. This threefold gap in half-precision throughput suggests the H800 SXM5 is built for dense tensor and matrix workloads, while the H20 NVL16's lower tensor core count and smaller ratio indicate a more modest AI compute envelope.

Memory presents a split decision. The H20 NVL16 offers 96 GB of HBM3 on a 6144-bit bus, yielding 4.03 TB/s of bandwidth. The H800 SXM5 has 80 GB on a 5120-bit bus, producing 3.36 TB/s. The H20 NVL16 leads in both capacity and bandwidth, which matters for models or datasets that exceed the H800's memory ceiling. Power consumption tells a complementary story: the H20 NVL16 draws 400 W with an 800 W suggested PSU, while the H800 SXM5 draws 700 W with a 1100 W suggested PSU. The H20 NVL16 achieves its higher clocks and memory throughput at lower power, indicating better efficiency per watt.

Architecture Differences

Both GPUs share the same fundamental silicon. The chip is GH100, the architecture is Hopper, the process node is 5 nm, and the foundry is TSMC. Transistor counts match at 80,000 million, die size matches at 814 mm², and transistor density matches at 98.3M per mm². Both use HBM3 memory, both are SXM modules, both use PCIe 5.0 x16, and both have no display outputs.

The differences emerge in how the silicon is configured. The H20 NVL16 has a 6144-bit memory bus, the H800 SXM5 has a 5120-bit bus. The H20 NVL16's memory runs at 1313 MHz with 5.3 Gbps effective, identical to the H800 SXM5's memory clock. The H20 NVL16's 96 GB capacity versus the H800 SXM5's 80 GB is the most direct memory divergence.

Clock behavior diverges sharply. The H20 NVL16 has a base clock of 1830 MHz, which is 735 MHz higher than the H800 SXM5's base. The boost clock difference is smaller but still notable: 1980 MHz versus 1755 MHz, a 225 MHz gap. This suggests the H20 NVL16 is binned or configured for higher sustained frequencies, while the H800 SXM5 relies on a larger core count operating at lower clocks.

The execution unit counts differ by a factor of roughly 1.7. The H800 SXM5 has 16896 shading units versus 9984, 528 TMUs versus 312, and 528 tensor cores versus 312. Both parts have 24 ROPs. The H800 SXM5's FP16 rating of 237.2 TFLOPS at 4:1 indicates a doubled tensor throughput relative to its FP32, while the H20 NVL16's FP16 at 2:1 indicates the same tensor core count handles FP16 at only twice the FP32 rate. The H800 SXM5's tensor cores are evidently capable of higher per-core throughput or use a different accumulation scheme.

The H800 SXM5 requires an 8-pin EPS power connector, while the H20 NVL16's power connector field is null. The H800 SXM5's TDP of 700 W and suggested PSU of 1100 W contrast with the H20 NVL16's 400 W TDP and 800 W suggested PSU. The H20 NVL16's lower power envelope is consistent with fewer active execution units, despite its higher clock speeds.

FAQ

Q: Which GPU has more memory bandwidth?

A: The NVIDIA H20 NVL16 leads with 4.03 TB/s, while the NVIDIA H800 SXM5 delivers 3.36 TB/s.

Q: How do the FP32 compute ratings compare?

A: The H800 SXM5 reaches 59.30 TFLOPS, which is higher than the H20 NVL16's 39.54 TFLOPS.

Q: What is the difference in memory capacity?

A: The H20 NVL16 has 96 GB, and the H800 SXM5 has 80 GB, a 16 GB gap.

Q: Which GPU has a higher boost clock?

A: The H20 NVL16 boosts to 1980 MHz, while the H800 SXM5 boosts to 1755 MHz.

Q: Are both GPUs built on the same process node?

A: Yes, both use TSMC's 5 nm process with the GH100 chip and Hopper architecture.

Q: What are the power requirements?

A: The H20 NVL16 is rated at 400 W with an 800 W suggested PSU, and the H800 SXM5 is rated at 700 W with a 1100 W suggested PSU.

Specification Differences

The two GPUs share identical silicon-level attributes: chip GH100, architecture Hopper, 5 nm process, TSMC foundry, 80,000 million transistors, 814 mm² die size, and 98.3M transistors per mm². Both use HBM3 memory, PCIe 5.0 x16, and the SXM module form factor. Both have no display outputs and 24 ROPs.

The differences are enumerated below:

• Base clock: 1830 MHz (H20) versus 1095 MHz (H800)
• Boost clock: 1980 MHz (H20) versus 1755 MHz (H800)
• Memory size: 96 GB (H20) versus 80 GB (H800)
• Memory bus width: 6144 bit (H20) versus 5120 bit (H800)
• Memory bandwidth: 4.03 TB/s (H20) versus 3.36 TB/s (H800)
• Shading units: 9984 (H20) versus 16896 (H800)
• Texture mapping units: 312 (H20) versus 528 (H800)
• Tensor cores: 312 (H20) versus 528 (H800)
• Pixel rate: 47.52 GPixel/s (H20) versus 42.12 GPixel/s (H800)
• Texture rate: 617.8 GTexel/s (H20) versus 926.6 GTexel/s (H800)
• FP32 performance: 39.54 TFLOPS (H20) versus 59.30 TFLOPS (H800)
• FP16 performance: 79.07 TFLOPS at 2:1 (H20) versus 237.2 TFLOPS at 4:1 (H800)
• TDP: 400 W (H20) versus 700 W (H800)
• Suggested PSU: 800 W (H20) versus 1100 W (H800)
• Power connector: none listed (H20) versus 8-pin EPS (H800)
• Release date: 2025-09-01 (H20) versus 2023-03-20 (H800)

DirectX, OpenGL, and Vulkan API support are listed as N/A for the H20 NVL16 and null for the H800 SXM5, both indicating no graphics API support. The memory clock is identical at 1313 MHz with 5.3 Gbps effective. Both share the same generation, predecessor, and successor designations.

Head-to-Head Benchmarks

The head-to-head benchmark array contains no entries, and both parts record zero wins. This absence of measured data means the performance comparison must be derived from the recorded specification fields.

The most decisive win for the H800 SXM5 is in FP16 throughput. At 237.2 TFLOPS, it is exactly three times the H20 NVL16's 79.07 TFLOPS. This is the largest proportional gap in any compute metric. The H800 SXM5's FP32 output of 59.30 TFLOPS is 50% higher than the H20 NVL16's 39.54 TFLOPS. Texture rate follows a similar pattern: 926.6 GTexel/s versus 617.8 GTexel/s, a 50% advantage for the H800 SXM5.

The H20 NVL16 wins on memory bandwidth by 19.9%, delivering 4.03 TB/s against 3.36 TB/s. It also wins on memory capacity by 20%, at 96 GB versus 80 GB. Pixel rate favors the H20 NVL16 at 47.52 GPixel/s versus 42.12 GPixel/s, a 12.8% margin. The H20 NVL16's boost clock is 12.8% higher, and its base clock is 67.1% higher.

Power efficiency favors the H20 NVL16. At 400 W, it delivers 4.03 TB/s of bandwidth, while the H800 SXM5 at 700 W delivers 3.36 TB/s. The H20 NVL16 produces 0.0101 TB/s per watt, and the H800 SXM5 produces 0.0048 TB/s per watt. Similarly, FP32 per watt is 0.0989 TFLOPS/W for the H20 NVL16 versus 0.0847 TFLOPS/W for the H800 SXM5. The H20 NVL16 is more efficient on both metrics, despite its lower absolute compute.

The compute-per-bandwidth ratio tells a different story. The H800 SXM5 has 59.30 TFLOPS of FP32 against 3.36 TB/s, a ratio of 17.6 TFLOPS per TB/s. The H20 NVL16 has 39.54 TFLOPS against 4.03 TB/s, a ratio of 9.8 TFLOPS per TB/s. The H800 SXM5 is compute-heavy relative to its memory, while the H20 NVL16 is memory-heavy relative to its compute. This indicates different design intents: the H800 SXM5 for dense compute, the H20 NVL16 for memory-bound workloads.

The Verdict

The data indicates two distinct usage profiles. The NVIDIA H800 SXM5 is the compute specialist. Its 16896 shading units, 528 tensor cores, and 237.2 TFLOPS FP16 rating make it the choice for workloads that saturate tensor or FP32 execution. Its higher 700 W TDP and 1100 W suggested PSU reflect the cost of that capability. The H800 SXM5's lower clocks are compensated by the sheer number of cores, and the 4:1 FP16 ratio suggests it is optimized for mixed-precision training or inference where FP16 throughput is the bottleneck.

The NVIDIA H20 NVL16 is the memory and efficiency specialist. Its 96 GB capacity and 4.03 TB/s bandwidth exceed the H800 SXM5 in both dimensions, and it achieves this at 400 W, nearly half the power draw. The higher base and boost clocks give it a pixel-rate advantage, but the smaller execution unit counts cap its compute throughput. The FP16 ratio of 2:1 indicates tensor cores that process FP16 at half the rate of the H800 SXM5's.

The release dates reinforce this split. The H800 SXM5 launched on 2023-03-20, while the H20 NVL16 launched on 2025-09-01. The later release of the H20 NVL16 with lower compute but higher memory and efficiency suggests a deliberate rebalancing toward memory-capacity-limited workloads, such as large language model inference or data-intensive analytics. The H800 SXM5, released earlier, targets the traditional high-compute server segment.

There is no benchmark data to confirm real-world behavior, so the verdict rests on the specification record. For dense FP16 or FP32 compute, the H800 SXM5 is the stronger part by a wide margin. For memory capacity, bandwidth, and power efficiency, the H20 NVL16 holds the advantage. The choice depends on which resource is the constraint: compute throughput or memory footprint. The data cannot resolve that trade-off, but it clearly delineates the two paths.