NVIDIA H100 CNX vs NVIDIA H20 NVL16 Comparison

Head-to-Head Benchmarks

The recorded data shows no direct benchmark entries for either the NVIDIA H100 CNX or the NVIDIA H20 NVL16. Both accelerators hold a database percentile ranking of 50 against all GPUs, and their average benchmark scores are recorded as zero. Consequently, the head-to-head comparison here is built from the specification-level measurements captured in the database, which reveal clear performance hierarchies in several compute domains.

The H100 CNX delivers a higher FP32 throughput at 53.84 TFLOPS, which is 36.2% ahead of the H20 NVL16's 39.54 TFLOPS. This advantage comes from a larger shader array: the H100 CNX has 14,592 shading units against the H20 NVL16's 9,984. The texture rate follows a similar pattern, with the H100 CNX producing 841.3 GTexel/s versus 617.8 GTexel/s for the H20 NVL16, a 36.2% margin that mirrors the shading unit difference. Pixel rates are closer: the H100 CNX achieves 44.28 GPixel/s, while the H20 NVL16 achieves 47.52 GPixel/s, putting the H20 NVL16 ahead by 7.3% despite its fewer ROPs (both have 24 ROPs), a result attributable to its higher boost clock.

The FP16 comparison flips dramatically. The H100 CNX records 215.4 TFLOPS at a 4:1 ratio, while the H20 NVL16 records 79.07 TFLOPS at a 2:1 ratio. Even accounting for the different ratios, the H100 CNX's FP16 figure is 2.7 times higher than the H20 NVL16's raw number. The H100 CNX also has more tensor cores: 456 versus 312 for the H20 NVL16. This combination suggests the H100 CNX is the stronger choice for workloads that depend on dense FP16 tensor operations, though the H20 NVL16's 2:1 ratio may indicate a design tuned for different precision handling.

Memory bandwidth is where the H20 NVL16 establishes its most decisive lead. The H20 NVL16 carries 96 GB of HBM3 across a 6,144-bit bus, yielding 4.03 TB/s of bandwidth. The H100 CNX has 80 GB of HBM2e on a 5,120-bit bus, yielding 2.04 TB/s. The H20 NVL16 therefore delivers 97.5% more bandwidth, nearly double, while also offering 20% more memory capacity. This is a substantial advantage for memory-bound inference and large model residency.

Clock speeds favor the H20 NVL16. Its base clock is 1,830 MHz and boost clock is 1,980 MHz, whereas the H100 CNX operates at 690 MHz base and 1,845 MHz boost. The H20 NVL16's base clock is 2.65 times higher than the H100 CNX's base clock, and its boost clock is 7.3% higher. The H20 NVL16 also has a faster memory clock at 1,313 MHz (5.3 Gbps effective) versus 1,593 MHz (3.2 Gbps effective) for the H100 CNX, although the H100 CNX's memory clock figure is expressed differently; the effective data rate is 65.6% higher on the H20 NVL16.

Power consumption differs as well: the H100 CNX has a TDP of 350 W, while the H20 NVL16 has a TDP of 400 W. The H20 NVL16 draws 14.3% more power, and its suggested PSU is 800 W versus 750 W for the H100 CNX. The H100 CNX uses a dual-slot form factor with an 8-pin EPS connector, while the H20 NVL16 is an SXM module without a listed power connector.

Where Each One Wins

The data indicates a clear split by workload type. The H100 CNX wins in raw compute throughput for single-precision and half-precision operations. Its FP32 output of 53.84 TFLOPS and FP16 output of 215.4 TFLOPS are the highest figures in this comparison, and the larger shading unit count (14,592 versus 9,984) gives it a structural edge for general parallel compute. The H100 CNX also has more tensor cores (456 versus 312), which matters for training workloads that rely on tensor core utilization. Its texture rate of 841.3 GTexel/s exceeds the H20 NVL16's 617.8 GTexel/s, so workloads with heavy texture sampling would favor the H100 CNX.

The H20 NVL16 wins in memory-intensive scenarios. Its 4.03 TB/s bandwidth is roughly double the H100 CNX's 2.04 TB/s, and its 96 GB capacity exceeds the H100 CNX's 80 GB. This makes the H20 NVL16 better suited for large model inference, where the entire model must reside in memory and bandwidth dictates throughput. The H20 NVL16's higher pixel rate (47.52 GPixel/s versus 44.28 GPixel/s) also gives it a small edge in any output-generation stage that depends on rasterization, though both cards lack display outputs.

The clock speed difference further reinforces the split. The H20 NVL16's boost clock of 1,980 MHz is 7.3% higher than the H100 CNX's 1,845 MHz, and its base clock is more than 2.6 times higher. For workloads that scale with clock frequency rather than core count, such as latency-sensitive inference steps, the H20 NVL16 should execute each operation faster. However, the H100 CNX's larger core count compensates in throughput-oriented tasks.

FAQ

Q: Which GPU has higher FP32 performance?

A: The H100 CNX records 53.84 TFLOPS FP32, which is 36.2% higher than the H20 NVL16's 39.54 TFLOPS.

Q: How do the memory bandwidth figures compare?

A: The H20 NVL16 has 4.03 TB/s bandwidth from 96 GB of HBM3 on a 6,144-bit bus, while the H100 CNX has 2.04 TB/s from 80 GB of HBM2e on a 5,120-bit bus. The H20 NVL16's bandwidth is 97.5% higher.

Q: What are the tensor core counts for each GPU?

A: The H100 CNX has 456 tensor cores, and the H20 NVL16 has 312 tensor cores.

Q: What is the difference in boost clock speeds?

A: The H20 NVL16 boosts to 1,980 MHz, which is 7.3% higher than the H100 CNX's 1,845 MHz boost clock.

Q: Which GPU has more shading units and by how much?

A: The H100 CNX has 14,592 shading units, which is 46.2% more than the H20 NVL16's 9,984 shading units.

Q: What are the TDP values for both GPUs?

A: The H100 CNX has a TDP of 350 W, and the H20 NVL16 has a TDP of 400 W.

Specification Differences

The two accelerators share the same GH100 chip, Hopper architecture, TSMC 5 nm process node, 80,000 million transistors, and 814 mm² die size. They also both use a PCIe 5.0 x16 bus interface and have no display outputs. Beyond those commonalities, the recorded specifications differ substantially.

Memory configuration is the most visible split. The H100 CNX uses 80 GB of HBM2e with a 5,120-bit bus and 2.04 TB/s bandwidth. The H20 NVL16 uses 96 GB of HBM3 with a 6,144-bit bus and 4.03 TB/s bandwidth. The H20 NVL16 has 16 GB more capacity and 1.99 TB/s more bandwidth.

Compute units differ as well. The H100 CNX has 14,592 shading units, 456 TMUs, 24 ROPs, and 456 tensor cores. The H20 NVL16 has 9,984 shading units, 312 TMUs, 24 ROPs, and 312 tensor cores. The H100 CNX leads by 4,608 shading units, 144 TMUs, and 144 tensor cores; ROPs are equal at 24.

Clock specifications diverge. The H100 CNX runs at 690 MHz base and 1,845 MHz boost. The H20 NVL16 runs at 1,830 MHz base and 1,980 MHz boost. Memory clocks also differ: the H100 CNX's memory operates at 1,593 MHz with 3.2 Gbps effective, while the H20 NVL16's memory operates at 1,313 MHz with 5.3 Gbps effective.

Performance rates follow the hardware differences. The H100 CNX produces 53.84 TFLOPS FP32 and 215.4 TFLOPS FP16 (4:1). The H20 NVL16 produces 39.54 TFLOPS FP32 and 79.07 TFLOPS FP16 (2:1). Pixel rates are 44.28 GPixel/s for the H100 CNX and 47.52 GPixel/s for the H20 NVL16. Texture rates are 841.3 GTexel/s and 617.8 GTexel/s, respectively.

Physical and power specifications also differ. The H100 CNX is a dual-slot card, 267 mm long and 111 mm high, with an 8-pin EPS power connector, a 350 W TDP, and a suggested PSU of 750 W. The H20 NVL16 is an SXM module with no listed dimensions, no listed power connector, a 400 W TDP, and a suggested PSU of 800 W.

Release dates are recorded differently: the H100 CNX has a release date of 2023-03-20, while the H20 NVL16 has a release date of 2025-09-01. Both are listed as Active in production status, both have Server Ada as predecessor and Server Blackwell as successor, and neither has a launch MSRP in the database.

Architecture Differences

Both GPUs are built on the GH100 chip using the Hopper architecture, which means they share the same fundamental design philosophy and manufacturing process. The TSMC 5 nm node, 80,000 million transistors, and 814 mm² die size are identical, resulting in a transistor density of 98.3M per mm² for both.

The architectural distinction lies in how each SKU configures the GH100 silicon. The H100 CNX activates a larger portion of the compute resources: 14,592 shading units and 456 tensor cores. The H20 NVL16 uses a more restricted configuration with 9,984 shading units and 312 tensor cores. This is a deliberate partitioning of the same physical die, where the H20 NVL16 sacrifices compute density for memory throughput.

Memory architecture is the other major differentiator. The H100 CNX uses HBM2e with an 80 GB capacity and a 5,120-bit bus. The H20 NVL16 uses HBM3 with a 96 GB capacity and a 6,144-bit bus. The HBM3 standard enables a higher effective data rate of 5.3 Gbps versus 3.2 Gbps for HBM2e, which explains the bandwidth gap between 4.03 TB/s and 2.04 TB/s despite the H20 NVL16's lower memory clock of 1,313 MHz versus 1,593 MHz.

The FP16 ratio difference (4:1 for the H100 CNX, 2:1 for the H20 NVL16) indicates different handling of half-precision operations. The H100 CNX's 215.4 TFLOPS FP16 at 4:1 suggests a configuration optimized for dense FP16 throughput, while the H20 NVL16's 79.07 TFLOPS at 2:1 implies a different precision scaling strategy, possibly to maintain efficiency across a wider range of precision formats.

The H20 NVL16's higher boost clock of 1,980 MHz versus 1,845 MHz for the H100 CNX, along with its significantly higher base clock of 1,830 MHz versus 690 MHz, suggests the H20 NVL16 is binned or configured for higher sustained clock operation. This may come at the cost of the lower compute unit count, as the power envelope of 400 W versus 350 W reflects an additional 14.3% power draw for the H20 NVL16.

The Verdict

The data supports a straightforward selection based on workload priorities. For compute-bound training and high-precision workloads, the H100 CNX is the stronger option. Its FP32 output is 36.2% higher than the H20 NVL16's, its FP16 output is 2.7 times higher, and it carries 46.2% more shading units and 46.2% more tensor cores. The 350 W TDP also means it consumes less power while delivering more compute throughput, an efficiency advantage in dense training clusters.

For memory-bound inference and large model deployment, the H20 NVL16 is the clear choice. Its 96 GB capacity and 4.03 TB/s bandwidth give it a 20% capacity advantage and a 97.5% bandwidth advantage over the H100 CNX. The higher boost clock of 1,980 MHz and the higher pixel rate of 47.52 GPixel/s further support scenarios where memory access and output generation dominate. The 400 W TDP is a modest increase over the H100 CNX's 350 W, and the SXM module form factor suggests a different integration path, likely for dense server configurations.

Both GPUs occupy the same database percentile rank of 50, which indicates that neither holds a categorical superiority across all metrics. The H100 CNX wins on compute density and precision throughput, while the H20 NVL16 wins on memory capacity, memory bandwidth, and clock speed. The choice hinges on whether the workload is dominated by arithmetic operations or by memory access patterns. The recorded specifications provide no evidence that either GPU can excel at both simultaneously, so the selection should be driven by the specific ratio of compute to memory demand in the target application.