NVIDIA A100 SXM4 40 GB vs NVIDIA PG506-232 Comparison

# NVIDIA PG506-232 vs NVIDIA A100 SXM4 40 GB

The NVIDIA PG506-232 and NVIDIA A100 SXM4 40 GB are both server-class Ampere accelerators built on the GA100 chip, yet they occupy distinctly different performance tiers. Benchmark data from Geekbench OpenCL shows the PG506-232 scoring 225,124 points against the A100 SXM4 40 GB's 201,096 points, a 11.9% advantage for the PG506-232. The A100 SXM4 40 GB also has a Geekbench Vulkan score of 173,198, which the PG506-232 lacks. Despite the A100 SXM4 40 GB carrying more CUDA cores, higher memory bandwidth, and double the FP32 throughput, the PG506-232 wins the sole head-to-head benchmark. This paradox stems from significant architectural and configuration differences between the two cards.

Where Each One Wins

The PG506-232 wins the only directly comparable benchmark in this dataset. Its Geekbench OpenCL score of 225,124 places it in the 99th percentile of all GPUs, while the A100 SXM4 40 GB's 201,096 OpenCL result lands in the 98th percentile. The PG506-232's 11.9% lead in OpenCL suggests it is better optimized for compute workloads that rely on this API, despite having fewer shading units (3584 vs 6912) and lower FP32 throughput (10.32 TFLOPS vs 19.49 TFLOPS).

The A100 SXM4 40 GB, however, has a clear edge in raw specification-driven scenarios. Its 40 GB of HBM2e memory with 1.56 TB/s bandwidth dwarfs the PG506-232's 24 GB of HBM2 at 933.1 GB/s. For memory-bound workloads — large language models, scientific simulations, or data analytics that require massive datasets in VRAM — the A100 SXM4 40 GB's memory capacity and bandwidth make it the superior choice. The PG506-232's 24 GB capacity may force frequent data transfers, which the A100 SXM4 40 GB avoids entirely.

The A100 SXM4 40 GB also wins on pure compute throughput. Its FP32 performance of 19.49 TFLOPS is nearly double the PG506-232's 10.32 TFLOPS. Its FP16 performance of 77.97 TFLOPS (4:1 ratio) is over seven times the PG506-232's 10.32 TFLOPS (1:1 ratio). For training or inference workloads that leverage FP16 or mixed precision — common in deep learning — the A100 SXM4 40 GB's tensor cores (432 vs 224) and FP16 capabilities provide a massive advantage.

Architecture Differences

Both cards share the same GA100 chip, 7 nm TSMC process, 54,200 million transistors, 826 mm² die size, and 65.6M transistors per mm² density. They also share the same PCIe 4.0 x16 bus interface, no display outputs, and no ray tracing cores. The production status is end-of-life for both, with the PG506-232 releasing on 2021-04-11 and the A100 SXM4 40 GB on 2020-05-13.

The critical architectural divergence lies in their implementation of the GA100 die. The PG506-232 uses 3584 shading units, 224 TMUs, and 96 ROPs, while the A100 SXM4 40 GB has 6912 shading units, 432 TMUs, and 160 ROPs. This is a 2x difference in shading units and TMUs, and a 1.67x difference in ROPs. The A100 SXM4 40 GB also has 432 tensor cores versus 224 on the PG506-232.

Clock speeds differ notably. The PG506-232 has a base clock of 930 MHz and boost clock of 1440 MHz, while the A100 SXM4 40 GB runs at 1095 MHz base and 1410 MHz boost. The PG506-232's higher boost clock partially compensates for its fewer cores, but the A100 SXM4 40 GB's higher base clock indicates sustained performance under load.

Memory architecture is fundamentally different. The PG506-232 uses 24 GB of HBM2 on a 3072-bit bus, while the A100 SXM4 40 GB uses 40 GB of HBM2e on a 5120-bit bus. Both operate at 1215 MHz with 2.4 Gbps effective, but the wider bus on the A100 SXM4 40 GB yields 1.56 TB/s bandwidth versus 933.1 GB/s — a 67% increase.

Power and physical design differ drastically. The PG506-232 is a dual-slot card with an 8-pin EPS connector and 165 W TDP, while the A100 SXM4 40 GB is an SXM module with no power connectors and a 400 W TDP. The PG506-232 measures 267 mm in length and 112 mm in height, while the A100 SXM4 40 GB has no listed dimensions. The suggested PSU ratings are 450 W for the PG506-232 and 800 W for the A100 SXM4 40 GB.

Head-to-Head Benchmarks

The only direct comparison in the dataset is Geekbench OpenCL, where the PG506-232 scores 225,124 against the A100 SXM4 40 GB's 201,096. This 11.9% delta is substantial, especially given the A100 SXM4 40 GB's superior raw specs. The PG506-232's OpenCL victory is likely driven by its higher boost clock (1440 MHz vs 1410 MHz) and possibly better driver optimization for the OpenCL workload.

The A100 SXM4 40 GB has an additional Geekbench Vulkan score of 173,198, which the PG506-232 lacks entirely. This indicates the A100 SXM4 40 GB supports Vulkan compute workloads, while the PG506-232 either lacks this capability or has not been benchmarked for it.

Contextualizing the PG506-232's OpenCL score against its nearest rivals shows it sits 2.4% ahead of the AMD Radeon PRO W7900D (219,827), 8.7% ahead of the NVIDIA A100 PCIe 80 GB (207,124), 10.4% behind the NVIDIA L20 (251,147), and 14.9% ahead of the NVIDIA RTX 6000D (195,964). The A100 SXM4 40 GB's OpenCL score of 201,096 is 1.3% ahead of the NVIDIA RTX 5000 Ada Generation (184,664), 1.9% ahead of the NVIDIA A100 SXM4 80 GB (183,725), 2.8% ahead of the NVIDIA RTX PRO 5000 Blackwell (182,109), and 3.7% behind the NVIDIA Tesla V100S PCIe 32 GB (194,415).

These rival relationships reveal that the PG506-232 outperforms even the A100 PCIe 80 GB in OpenCL, while the A100 SXM4 40 GB barely edges out its 80 GB sibling. This suggests the PG506-232 may have been specifically tuned for certain compute workloads, despite its lower peak specifications.

FAQ

Q: Which GPU has higher FP32 performance?

A: The NVIDIA A100 SXM4 40 GB delivers 19.49 TFLOPS FP32, nearly double the PG506-232's 10.32 TFLOPS. This makes the A100 SXM4 40 GB significantly faster for single-precision compute tasks.

Q: Why does the PG506-232 win the OpenCL benchmark despite having fewer cores?

A: The PG506-232's 225,124 OpenCL score versus 201,096 for the A100 SXM4 40 GB (11.9% delta) likely stems from its higher boost clock of 1440 MHz versus 1410 MHz, plus potential workload-specific optimizations. The A100 SXM4 40 GB's wider memory bus and more cores do not translate to an OpenCL win in this dataset.

Q: How do the memory configurations compare?

A: The A100 SXM4 40 GB has 40 GB of HBM2e on a 5120-bit bus with 1.56 TB/s bandwidth. The PG506-232 has 24 GB of HBM2 on a 3072-bit bus with 933.1 GB/s bandwidth. The A100 SXM4 40 GB offers 67% more memory and 67% more bandwidth.

Q: What are the power requirements for each card?

A: The PG506-232 has a 165 W TDP with an 8-pin EPS connector and a 450 W suggested PSU. The A100 SXM4 40 GB has a 400 W TDP, no power connectors (SXM module), and an 800 W suggested PSU.

Q: Which card is better for FP16 workloads?

A: The A100 SXM4 40 GB achieves 77.97 TFLOPS FP16 with a 4:1 ratio, while the PG506-232 reaches 10.32 TFLOPS FP16 at 1:1. The A100 SXM4 40 GB is over seven times faster for FP16 compute.

Q: Do both cards support the same APIs?

A: Both cards have no listed DirectX, OpenGL, or Vulkan API data. However, the A100 SXM4 40 GB has a Geekbench Vulkan score of 173,198, while the PG506-232 only has an OpenCL score, suggesting the A100 SXM4 40 GB has Vulkan support that the PG506-232 may lack.

Specification Differences

| Specification | NVIDIA PG506-232 | NVIDIA A100 SXM4 40 GB |

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

| Process Node | 7 nm | 7 nm |

| Transistors | 54,200 million | 54,200 million |

| Die Size | 826 mm² | 826 mm² |

| Base Clock | 930 MHz | 1095 MHz |

| Boost Clock | 1440 MHz | 1410 MHz |

| Memory Size | 24 GB | 40 GB |

| Memory Type | HBM2 | HBM2e |

| Memory Bus Width | 3072 bit | 5120 bit |

| Memory Bandwidth | 933.1 GB/s | 1.56 TB/s |

| Shading Units | 3584 | 6912 |

| TMUs | 224 | 432 |

| ROPs | 96 | 160 |

| Tensor Cores | 224 | 432 |

| Pixel Rate | 138.2 GPixel/s | 225.6 GPixel/s |

| Texture Rate | 322.6 GTexel/s | 609.1 GTexel/s |

| FP32 | 10.32 TFLOPS | 19.49 TFLOPS |

| FP16 | 10.32 TFLOPS (1:1) | 77.97 TFLOPS (4:1) |

| TDP | 165 W | 400 W |

| Slot Width | Dual-slot | SXM Module |

| Power Connectors | 8-pin EPS | None |

| Suggested PSU | 450 W | 800 W |

| Dimensions | 267 mm × 112 mm | Not listed |

| Release Date | 2021-04-11 | 2020-05-13 |

| OpenCL Score | 225,124 | 201,096 |

| Vulkan Score | Not listed | 173,198 |

| Percentile | 99th | 98th |