NVIDIA A100 PCIe 40 GB vs NVIDIA PG506-232 Comparison

The NVIDIA PG506-232 and the NVIDIA A100 PCIe 40 GB are both professional server accelerators built on the same GA100 chip and Ampere architecture, yet the benchmark data shows a clear performance hierarchy between them. In the available Geekbench OpenCL test, the PG506-232 delivers a score of 225,124, which is a substantial 26% higher than the A100 PCIe 40 GB’s 178,627. This decisive lead places the PG506-232 in the 99th percentile of all GPUs, while the A100 PCIe 40 GB sits at the 97th percentile, underscoring a meaningful gap in raw compute throughput despite their shared DNA.

Head-to-Head Benchmarks

The only direct comparison available is the Geekbench OpenCL benchmark, and it is a decisive victory for the PG506-232. The PG506-232 scores 225,124, while the A100 PCIe 40 GB trails at 178,627, resulting in a 26% delta in favor of the PG506-232. This is not a marginal difference; it represents a substantial performance advantage that will translate into faster completion of OpenCL-based workloads.

Contextualizing this result against their respective nearest rivals reinforces the magnitude of the PG506-232’s lead. The PG506-232 is 2.4% ahead of the AMD Radeon PRO W7900D (219,827) and 8.7% ahead of the NVIDIA A100 PCIe 80 GB (207,124), showing that its score is competitive even against higher-tier configurations. Conversely, the A100 PCIe 40 GB’s 178,627 score places it just 1.1% ahead of the AMD Radeon Pro W6800X (160,671), and it actually falls behind the AMD Radeon PRO W7800 (164,894) by 1.4%, the NVIDIA RTX A5500 (165,217) by 1.6%, and the NVIDIA RTX 4500 Ada Generation (166,094) by 2.2%. The PG506-232 is not just faster than the A100 PCIe 40 GB; it competes in a higher performance tier altogether.

The data also highlights that the A100 PCIe 40 GB’s single OpenCL score is not its only weakness. It also has a Geekbench Vulkan score of 146,380, a metric the PG506-232 lacks, meaning the PG506-232 does not offer a Vulkan result for comparison. However, for OpenCL workloads, the PG506-232’s 26% advantage is the dominant story in this head-to-head.

Where Each One Wins

The PG506-232 wins the only benchmark test available, making it the clear choice for compute-heavy tasks that leverage OpenCL. Its 26% score advantage over the A100 PCIe 40 GB suggests it will excel in general-purpose GPU computing where raw throughput is paramount. The PG506-232’s higher average benchmark score of 225,124, compared to the A100 PCIe 40 GB’s 162,504, further cements its position as the superior performer in synthetic workloads.

The A100 PCIe 40 GB, despite losing the head-to-head, has one distinct advantage: memory capacity. It offers 40 GB of HBM2e memory, whereas the PG506-232 is equipped with 24 GB of HBM2. For workloads that require large datasets to reside on the GPU, such as certain AI inference or data analytics tasks, the A100 PCIe 40 GB’s additional 16 GB of memory could be a determining factor, even if its compute speed is lower. The A100 PCIe 40 GB also has a Vulkan score of 146,380, providing an option for graphics or compute APIs that the PG506-232 does not support, effectively giving it a win in that specific API category by virtue of having a result at all.

Architecture Differences

Both processors are built on the GA100 chip using the Ampere architecture and are fabricated on TSMC’s 7 nm process node, with identical transistor counts of 54,200 million and a die size of 826 mm². The architectural core design is the same, but the configuration of resources differs significantly, which explains the performance gap.

The PG506-232 is configured with 3,584 shading units, 224 texture mapping units (TMUs), 96 render output units (ROPs), and 224 tensor cores. In contrast, the A100 PCIe 40 GB is significantly fuller-featured, with 6,912 shading units, 432 TMUs, 160 ROPs, and 432 tensor cores. The A100 PCIe 40 GB has nearly double the shading units and tensor cores, which should theoretically make it faster, yet the benchmark results show the opposite. This suggests that the PG506-232’s higher clock speeds are able to compensate for its lower core count in this specific test.

The memory subsystems also differ architecturally. The PG506-232 uses HBM2 memory with a 3072-bit bus width, while the A100 PCIe 40 GB uses HBM2e memory with a much wider 5120-bit bus. This results in a bandwidth of 933.1 GB/s for the PG506-232 versus 1.56 TB/s for the A100 PCIe 40 GB. Despite having lower bandwidth, the PG506-232 still wins the benchmark, indicating that memory bandwidth is not the limiting factor in this workload.

Specification Differences

The most glaring specification difference is memory configuration. The A100 PCIe 40 GB offers 40 GB of HBM2e memory compared to the PG506-232’s 24 GB of HBM2. The A100 PCIe 40 GB also has a much wider 5120-bit memory bus, delivering 1.56 TB/s of bandwidth, whereas the PG506-232 is limited to a 3072-bit bus and 933.1 GB/s.

Clock speeds are another key differentiator. The PG506-232 has a base clock of 930 MHz and a boost clock of 1440 MHz, while the A100 PCIe 40 GB runs lower at 765 MHz base and 1410 MHz boost. This higher clock speed is likely a primary reason for the PG506-232’s benchmark victory.

Compute specifications are vastly different. The PG506-232 delivers 10.32 TFLOPS of FP32 performance and 10.32 TFLOPS of FP16 (1:1), while the A100 PCIe 40 GB outputs 19.49 TFLOPS of FP32 and an enormous 77.97 TFLOPS of FP16 (4:1). The A100 PCIe 40 GB’s FP16 capabilities are dramatically higher, suggesting it is better suited for tensor-heavy AI workloads, despite losing the OpenCL test.

Power and physical dimensions also differ. The PG506-232 has a TDP of 165 W with a suggested PSU of 450 W, while the A100 PCIe 40 GB draws more power at 250 W TDP and requires a 600 W PSU. Both are dual-slot cards with identical 267 mm length, but the PG506-232 is 112 mm tall versus the A100 PCIe 40 GB’s 111 mm. Both use an 8-pin EPS power connector and have no display outputs. Finally, the A100 PCIe 40 GB was released earlier, on 2020-06-21, while the PG506-232 came later on 2021-04-11.

FAQ

Q: Which GPU has a higher benchmark score in OpenCL?

A: The NVIDIA PG506-232 scores 225,124 in Geekbench OpenCL, which is 26% higher than the NVIDIA A100 PCIe 40 GB’s score of 178,627.

Q: Does the A100 PCIe 40 GB have more memory than the PG506-232?

A: Yes, the A100 PCIe 40 GB has 40 GB of HBM2e memory, while the PG506-232 has 24 GB of HBM2 memory.

Q: What is the difference in FP16 performance?

A: The A100 PCIe 40 GB delivers 77.97 TFLOPS of FP16 (4:1), which is vastly higher than the PG506-232’s 10.32 TFLOPS of FP16 (1:1).

Q: Which GPU has higher clock speeds?

A: The PG506-232 has a base clock of 930 MHz and a boost clock of 1440 MHz, both higher than the A100 PCIe 40 GB’s 765 MHz base and 1410 MHz boost.

Q: How does the A100 PCIe 40 GB compare to its nearest rivals?

A: The A100 PCIe 40 GB is 1.1% ahead of the AMD Radeon Pro W6800X but falls behind the AMD Radeon PRO W7800 by 1.4%, the NVIDIA RTX A5500 by 1.6%, and the NVIDIA RTX 4500 Ada Generation by 2.2%.

Q: What is the power consumption difference?

A: The PG506-232 has a TDP of 165 W and a suggested PSU of 450 W, while the A100 PCIe 40 GB has a TDP of 250 W and a suggested PSU of 600 W.

The Verdict

Based strictly on the benchmark data, the NVIDIA PG506-232 is the clear winner for raw OpenCL compute performance. Its 26% higher score and 99th percentile ranking make it the superior choice for workloads that rely on this API, and it places in a higher performance tier than the A100 PCIe 40 GB, which sits at the 97th percentile. The PG506-232’s higher clock speeds and lower core count configuration proved more effective in this test, despite the A100 PCIe 40 GB having nearly double the shading units and tensor cores.

The NVIDIA A100 PCIe 40 GB, however, holds advantages where the PG506-232 cannot compete. Its 40 GB of memory is a significant upgrade over the PG506-232’s 24 GB, and its 1.56 TB/s memory bandwidth dwarfs the PG506-232’s 933.1 GB/s. For users with large memory footprints or those leveraging FP16 tensor operations, the A100 PCIe 40 GB’s 77.97 TFLOPS of FP16 performance makes it a more capable tool for AI and deep learning workloads, even if it loses the OpenCL contest.

Choose the PG506-232 if your primary concern is maximizing OpenCL throughput and you can operate within a 24 GB memory limit. Choose the A100 PCIe 40 GB if you need the larger memory capacity, higher bandwidth, or substantially greater FP16 compute power, and you are willing to accept a slower OpenCL result and higher power draw. The data does not support one GPU being universally better; it supports a clear split based on workload requirements.