NVIDIA PG506-232 vs NVIDIA Tesla V100 SXM2 16 GB Comparison

Head-to-Head Benchmarks

The recorded benchmark data contains a single direct comparison between the NVIDIA PG506-232 and the NVIDIA Tesla V100 SXM2 16 GB: the Geekbench OpenCL test. In this workload, the PG506-232 scores 225,124 points, while the Tesla V100 SXM2 scores 87,454 points. The delta between them is 157.4%, meaning the PG506-232 more than doubles the OpenCL performance of the older Volta part. This is a decisive margin, not a marginal improvement.

Looking at the broader database context, the PG506-232 sits in the 99th percentile of all GPUs, placing it among the top 1% of recorded devices. Its nearest rival in the database is the NVIDIA L20, which scores 251,147 and outperforms it by 10.4%. The PG506-232 is also ahead of the AMD Radeon PRO W7900D by 2.4% (219,827), the NVIDIA A100 PCIe 80 GB by 8.7% (207,124), and the NVIDIA RTX 6000D by 14.9% (195,964). These comparisons show that the PG506-232 is not merely fast in isolation; it consistently outpaces a range of modern workstation and data center accelerators.

The Tesla V100 SXM2, by contrast, sits in the 95th percentile, which is still strong but notably lower. Its average benchmark score across all recorded tests is 114,395, derived from its OpenCL result of 87,454 and a Vulkan score of 141,336. Its nearest rivals include the NVIDIA RTX A5500 Mobile (113,944, only 0.4% behind), the NVIDIA RTX 4000 SFF Ada Generation (117,088, 2.3% ahead), the NVIDIA GB10 (117,393, 2.6% ahead), and the AMD Radeon PRO W7900 (110,725, 3.3% behind). The V100 SXM2 is therefore clustered tightly with mid-to-high-end mobile and compact workstation parts, rather than competing with the top-tier accelerators where the PG506-232 operates.

The head-to-head data shows the PG506-232 wins the only recorded comparison, with winsA equal to 1 and winsB equal to 0. The 157.4% delta is the largest margin seen in the PG506-232's nearest rival group, far exceeding the 14.9% gap to the RTX 6000D. In practical terms, this means that for OpenCL-based compute workloads, the PG506-232 delivers more than two and a half times the raw score of the V100 SXM2, a gap that would translate into substantially shorter processing times for tasks that scale linearly with compute throughput.

Architecture Differences

The two GPUs come from different architectural generations and manufacturing processes. The PG506-232 is built on the Ampere architecture, using the GA100 chip, and is fabricated on a 7 nm process at TSMC. The Tesla V100 SXM2 uses the Volta architecture with the GV100 chip, on a 12 nm process, also at TSMC. This process difference is significant: the PG506-232 packs 54,200 million transistors into a die size of 826 mm², yielding a transistor density of 65.6 million per mm². The V100 SXM2 contains 21,100 million transistors on a die of 815 mm², resulting in a density of 25.9 million per mm². The Ampere chip achieves more than double the transistor density, allowing for more compute resources in a similar physical footprint.

The memory subsystems differ in capacity and bus width. The PG506-232 has 24 GB of HBM2 memory on a 3072-bit bus, delivering 933.1 GB/s of bandwidth. The V100 SXM2 has 16 GB of HBM2 on a wider 4096-bit bus, delivering 897.0 GB/s. Despite the wider bus on the V100, the PG506-232 still achieves higher total bandwidth due to faster memory clocks: 1215 MHz (2.4 Gbps effective) versus 876 MHz (1752 Mbps effective).

Compute resources are distributed differently. The PG506-232 has 3584 shading units, 224 texture mapping units, 96 ROPs, and 224 tensor cores. The V100 SXM2 has more of each: 5120 shading units, 320 TMUs, 128 ROPs, and 640 tensor cores. However, clock speeds tell a different story. The PG506-232 runs at a base of 930 MHz and a boost of 1440 MHz, while the V100 SXM2 runs at 1312 MHz base and 1530 MHz boost. The V100's higher clocks and larger shader count give it a theoretical FP32 throughput of 15.67 TFLOPS, compared to 10.32 TFLOPS for the PG506-232. For FP16, the V100 achieves 31.33 TFLOPS with a 2:1 ratio, while the PG506-232 achieves 10.32 TFLOPS with a 1:1 ratio.

Pixel and texture rates follow the same pattern: the V100 SXM2 reaches 195.8 GPixel/s and 489.6 GTexel/s, while the PG506-232 reaches 138.2 GPixel/s and 322.6 GTexel/s. These differences suggest that the V100 has a raw compute advantage in shader-heavy workloads, but the PG506-232's OpenCL benchmark result shows that real-world performance does not simply follow theoretical specs. The PG506-232 also uses a PCIe 4.0 x16 interface, while the V100 SXM2 uses PCIe 3.0 x16. Power and physical design differ as well: the PG506-232 is a dual-slot card with an 8-pin EPS connector and a TDP of 165 W, while the V100 SXM2 is an SXM module with no power connectors and a TDP of 250 W.

FAQ

Q: Which GPU has the higher OpenCL benchmark score?

A: The NVIDIA PG506-232 scores 225,124 in Geekbench OpenCL, versus 87,454 for the Tesla V100 SXM2. That is a 157.4% advantage for the PG506-232.

Q: Does the Tesla V100 SXM2 have any compute advantage over the PG506-232?

A: Yes, in theoretical specs. The V100 SXM2 has 5120 shading units versus 3584, and higher clock speeds (1530 MHz boost versus 1440 MHz). Its FP32 throughput is 15.67 TFLOPS and FP16 is 31.33 TFLOPS, compared to 10.32 TFLOPS for both FP32 and FP16 on the PG506-232.

Q: How do the memory configurations compare?

A: The PG506-232 has 24 GB of HBM2 on a 3072-bit bus with 933.1 GB/s bandwidth. The V100 SXM2 has 16 GB of HBM2 on a 4096-bit bus with 897.0 GB/s. The PG506-232 has more capacity and higher bandwidth.

Q: Which GPU is more power efficient according to the data?

A: The PG506-232 has a TDP of 165 W, while the V100 SXM2 has a TDP of 250 W. The PG506-232 achieves its higher OpenCL score with lower power consumption.

Q: What are the transistor density differences?

A: The PG506-232 uses a 7 nm process with 54,200 million transistors on 826 mm², giving a density of 65.6 million per mm². The V100 SXM2 uses a 12 nm process with 21,100 million transistors on 815 mm², giving a density of 25.9 million per mm².

Q: Does the V100 SXM2 support more API features?

A: The V100 SXM2 lists DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4 support. The PG506-232 has no API entries recorded in the database.

The Verdict

The benchmark data points to a clear overall winner for compute workloads: the NVIDIA PG506-232. Its OpenCL score of 225,124 versus 87,454 for the V100 SXM2 represents a 157.4% improvement, and it ranks in the 99th percentile of all GPUs versus the 95th percentile for the V100. The PG506-232 also holds advantages in memory capacity (24 GB vs 16 GB), bandwidth (933.1 GB/s vs 897.0 GB/s), and power efficiency (165 W vs 250 W TDP).

However, the V100 SXM2 is not without merit. Its theoretical compute throughput is substantially higher: 15.67 TFLOPS FP32 and 31.33 TFLOPS FP16, versus 10.32 TFLOPS for both on the PG506-232. It also has more shading units, TMUs, ROPs, and tensor cores. For workloads that are heavily shader-bound or rely on FP16 throughput, the V100's architecture may still be competitive, despite its lower benchmark score.

The production status of both parts is end-of-life, so neither is a forward-looking purchase. The PG506-232 is the newer design (released in April 2021 versus June 2017) and benefits from the Ampere architecture and 7 nm process. The V100 SXM2 is a Volta-era part that has been superseded. For users choosing between these two based solely on recorded data, the PG506-232 is the stronger choice for general compute and OpenCL workloads, while the V100 SXM2 retains a theoretical edge in specific FP16 or shader-heavy scenarios.

Specification Differences

The following fields differ between the two GPUs:

• Architecture: Ampere (PG506-232) vs Volta (V100 SXM2)
• Chip: GA100 vs GV100
• Process Node: 7 nm vs 12 nm
• Transistors: 54,200 million vs 21,100 million
• Die Size: 826 mm² vs 815 mm²
• Transistor Density: 65.6M / mm² vs 25.9M / mm²
• Base Clock: 930 MHz vs 1312 MHz
• Boost Clock: 1440 MHz vs 1530 MHz
• Memory Clock: 1215 MHz (2.4 Gbps effective) vs 876 MHz (1752 Mbps effective)
• Memory Size: 24 GB vs 16 GB
• Memory Bus Width: 3072 bit vs 4096 bit
• Memory Bandwidth: 933.1 GB/s vs 897.0 GB/s
• Shading Units: 3584 vs 5120
• TMUs: 224 vs 320
• ROPs: 96 vs 128
• Tensor Cores: 224 vs 640
• Pixel Rate: 138.2 GPixel/s vs 195.8 GPixel/s
• Texture Rate: 322.6 GTexel/s vs 489.6 GTexel/s
• FP32: 10.32 TFLOPS vs 15.67 TFLOPS
• FP16: 10.32 TFLOPS (1:1) vs 31.33 TFLOPS (2:1)
• TDP: 165 W vs 250 W
• Slot Width: Dual-slot vs SXM Module
• Power Connectors: 8-pin EPS vs None
• Suggested PSU: 450 W vs 600 W
• Bus Interface: PCIe 4.0 x16 vs PCIe 3.0 x16
• Release Date: 2021-04-11 vs 2017-06-20
• Predecessor: Tesla Turing vs Tesla Pascal
• Successor: Server Ada vs Tesla Turing
• APIs: None listed vs DirectX 12 (12_1), OpenGL 4.6, Vulkan 1.4

Where Each One Wins

The PG506-232 wins in the only recorded benchmark, the Geekbench OpenCL test, by a 157.4% margin. It also wins on memory capacity (24 GB vs 16 GB), memory bandwidth (933.1 GB/s vs 897.0 GB/s), power efficiency (165 W vs 250 W), and connector requirements (450 W suggested PSU vs 600 W). Its PCIe 4.0 interface offers double the bus bandwidth of the V100's PCIe 3.0, which can benefit data transfer in server environments. The PG506-232 is also the more recent design, with a release date in April 2021 versus June 2017, and it has higher transistor density from the 7 nm process.

The V100 SXM2 wins on raw compute specifications. It has more shading units (5120 vs 3584), more TMUs (320 vs 224), more ROPs (128 vs 96), and more tensor cores (640 vs 224). Its FP32 throughput is 15.67 TFLOPS, which is 51.8% higher than the PG506-232's 10.32 TFLOPS. Its FP16 throughput of 31.33 TFLOPS is more than triple that of the PG506-232. The V100 also has a wider memory bus (4096 bit vs 3072 bit) and higher pixel and texture rates (195.8 GPixel/s and 489.6 GTexel/s versus 138.2 GPixel/s and 322.6 GTexel/s). It lists API support for DirectX 12, OpenGL 4.6, and Vulkan 1.4, while the PG506-232 has none recorded.

In practical terms, the PG506-232 is the better choice for OpenCL-based compute tasks, memory-heavy workloads, and deployments where power draw matters. The V100 SXM2 retains a theoretical edge for shader-bound rendering, FP16 machine learning inference, or any workload that can fully utilize its larger shader and tensor core counts. Given that the PG506-232's actual OpenCL score is so much higher despite lower theoretical specs, the data suggests that the Ampere architecture's efficiency and memory advantages translate into superior real-world performance for the tested workload.