NVIDIA PG506-232 vs NVIDIA Tesla P100 PCIe 16 GB Comparison

The NVIDIA PG506-232 and the NVIDIA Tesla P100 PCIe 16 GB are both end-of-life server accelerators with no display outputs, but they belong to different architectural generations and deliver vastly different compute results. Benchmark data records a single OpenCL workload for both cards, and in that test the PG506-232 achieves a score of 225124, while the Tesla P100 scores 79605. That is a difference of 182.8 percent, a decisive gap that defines the entire comparison. The PG506-232 sits in the 99th percentile of all GPUs, while the Tesla P100 sits in the 92nd percentile. The following analysis breaks down where each card wins, what the recorded numbers mean, and which users should choose which based strictly on the database.

Where Each One Wins

The PG506-232 wins the only benchmark recorded in the database. In the Geekbench OpenCL test, the PG506-232 scores 225124 versus 79605 for the Tesla P100, a 182.8 percent advantage. This is not a marginal lead; it is a near-tripling of the raw score. The PG506-232 also holds a higher percentile ranking, at 99 compared to 92, which places it among the top tier of all recorded GPUs. The Tesla P100, by contrast, sits closer to the middle of the upper range, and its nearest rivals include the GeForce RTX 5090 at 79842, which is only 0.3 percent ahead, and the AMD Radeon RX 6850M XT at 78940, which is 0.8 percent behind. The PG506-232’s nearest rivals are much further away in raw score, but the PG506-232 is still ahead of them: the AMD Radeon PRO W7900D scores 219827, which is 2.4 percent lower, and the NVIDIA A100 PCIe 80 GB scores 207124, which is 8.7 percent lower. The only rival in the PG506-232’s nearest group that beats it is the NVIDIA L20 at 251147, which is 10.4 percent higher.

In terms of where each card wins, the data is unambiguous. The PG506-232 wins the compute workload decisively. The Tesla P100 does not win any recorded benchmark, so there is no measured workload where it comes out ahead in this comparison. However, the Tesla P100 does have a different memory configuration and a different FP16 capability profile, which are architectural traits rather than benchmark wins. The Tesla P100 has a 4096-bit memory bus, wider than the PG506-232’s 3072-bit bus, but the PG506-232 still achieves higher memory bandwidth at 933.1 GB/s versus 732.2 GB/s. The Tesla P100 has a 2:1 FP16 rate, meaning it can process half-precision data at double the rate of its FP32 throughput, reaching 19.05 TFLOPS, while the PG506-232 has a 1:1 FP16 rate at 10.32 TFLOPS. So in a hypothetical FP16-heavy workload, the Tesla P100’s architecture could be more efficient in terms of raw half-precision throughput, but no such benchmark is recorded in the database, and the only measured result favors the PG506-232.

The PG506-232 also wins on process technology and transistor count. It uses a 7 nm process from TSMC, while the Tesla P100 uses a 16 nm process from the same foundry. The PG506-232 packs 54,200 million transistors into an 826 mm² die, giving a transistor density of 65.6 million per square millimeter. The Tesla P100 has 15,300 million transistors on a 610 mm² die, for a density of 25.1 million per square millimeter. These numbers explain part of the performance gap, but the benchmark result is the direct evidence.

The Verdict

The verdict from the recorded data is straightforward: the NVIDIA PG506-232 outperforms the NVIDIA Tesla P100 PCIe 16 GB in the only measured benchmark by 182.8 percent. Anyone choosing between these two cards for a compute workload that resembles the Geekbench OpenCL test should select the PG506-232 without hesitation. Its score of 225124 places it in the 99th percentile of all GPUs, and it is ahead of strong competitors like the A100 PCIe 80 GB by 8.7 percent and the RTX 6000D by 14.9 percent. The Tesla P100, with a score of 79605, is in the 92nd percentile, and its nearest rivals are much closer in performance, within a range of about 1.7 percent either way. That suggests the Tesla P100 is roughly comparable to a much older or mid-range class of hardware, while the PG506-232 competes with modern high-end accelerators.

For users who already own a Tesla P100 and are considering an upgrade, the PG506-232 represents a massive step forward in measured compute performance. For users deciding between the two for a new deployment, the PG506-232 is the clear choice based on the benchmark data. The Tesla P100 does have a lower launch MSRP of 5,699 USD, which can be stated once as a reference point, but the performance gap is so large that the PG506-232’s advantage in the recorded workload dwarfs any consideration of the older card’s lower entry price. The Tesla P100 also has a lower power draw at 250 W versus 165 W for the PG506-232, which is an interesting reversal: the newer card consumes less power while delivering far higher performance. The PG506-232 has a suggested PSU of 450 W, while the Tesla P100 suggests 600 W.

The data does not support any scenario where the Tesla P100 wins a measured performance comparison. It wins no recorded benchmarks. Its only advantages are architectural, such as the wider 4096-bit memory bus, the 2:1 FP16 throughput, and the presence of DirectX 12, OpenGL 4.6, and Vulkan 1.3 APIs, none of which are reflected in the recorded OpenCL score. For a user who needs those specific API features, the Tesla P100 might be relevant, but for raw compute performance as measured, the PG506-232 is the winner.

Head-to-Head Benchmarks

The head-to-head comparison in the database contains exactly one test: Geekbench OpenCL. The PG506-232 scores 225124, and the Tesla P100 scores 79605. The delta is 182.8 percent, meaning the PG506-232’s score is nearly three times that of the Tesla P100. This is the largest margin in the entire comparison, and it defines the relationship between the two cards.

To put that score in context, the PG506-232’s nearest rivals include the NVIDIA L20 at 251147, which is 10.4 percent higher, and the AMD Radeon PRO W7900D at 219827, which is 2.4 percent lower. The Tesla P100’s nearest rivals include the GeForce RTX 5090 at 79842, which is 0.3 percent higher, and the AMD Radeon Pro Vega 64X at 80959, which is 1.7 percent higher. In other words, the PG506-232 operates in a performance class where its competitors are modern, high-end accelerators, while the Tesla P100 operates in a class where its competitors are within a few percentage points of its score. The PG506-232 is not just ahead of the Tesla P100; it is in a completely different performance tier.

The benchmark result also aligns with the architectural differences. The PG506-232 has a boost clock of 1440 MHz, while the Tesla P100 has a boost clock of 1329 MHz. The PG506-232 has a base clock of 930 MHz versus 1190 MHz for the Tesla P100, so the older card actually starts at a higher frequency, but the newer card boosts higher. The PG506-232 has 933.1 GB/s of memory bandwidth versus 732.2 GB/s for the Tesla P100. The PG506-232 has 3584 shading units, 224 texture mapping units, and 96 ROPs, exactly the same counts as the Tesla P100. The PG506-232 also has 224 tensor cores, while the Tesla P100 has none. That is a major architectural difference: the PG506-232 supports tensor operations, which are absent from the Tesla P100.

The PG506-232 achieves a pixel rate of 138.2 GPixel/s and a texture rate of 322.6 GTexel/s, while the Tesla P100 achieves 127.6 GPixel/s and 297.7 GTexel/s. Both rates are higher on the PG506-232, though the differences are smaller than the OpenCL score gap. The FP32 throughput is 10.32 TFLOPS for the PG506-232 and 9.526 TFLOPS for the Tesla P100, a modest difference. The FP16 throughput, however, is where the architectures diverge: the PG506-232 runs FP16 at 10.32 TFLOPS with a 1:1 ratio, while the Tesla P100 runs FP16 at 19.05 TFLOPS with a 2:1 ratio. That means the Tesla P100 has a higher theoretical FP16 peak, but the recorded OpenCL benchmark does not reflect that, and no FP16-specific test is present in the database.

FAQ

Q: Which card has the higher benchmark score?

A: The NVIDIA PG506-232 scores 225124 in the Geekbench OpenCL test, while the NVIDIA Tesla P100 PCIe 16 GB scores 79605. The PG506-232 is 182.8 percent higher.

Q: How do the two cards compare to their nearest rivals?

A: The PG506-232 is 2.4 percent ahead of the AMD Radeon PRO W7900D, 8.7 percent ahead of the NVIDIA A100 PCIe 80 GB, and 14.9 percent ahead of the NVIDIA RTX 6000D, but 10.4 percent behind the NVIDIA L20. The Tesla P100 is 0.3 percent behind the GeForce RTX 5090, 0.8 percent ahead of the AMD Radeon RX 6850M XT, and 1.7 percent behind the AMD Radeon Pro Vega 64X.

Q: What are the memory differences between the two cards?

A: The PG506-232 has 24 GB of HBM2 memory with a 3072-bit bus and 933.1 GB/s bandwidth. The Tesla P100 has 16 GB of HBM2 memory with a 4096-bit bus and 732.2 GB/s bandwidth. The PG506-232 has more capacity and higher bandwidth, while the Tesla P100 has a wider bus.

Q: Does either card support tensor cores?

A: The PG506-232 has 224 tensor cores. The Tesla P100 has no tensor cores.

Q: What is the FP16 throughput for each card?

A: The PG506-232 delivers 10.32 TFLOPS FP16 with a 1:1 ratio. The Tesla P100 delivers 19.05 TFLOPS FP16 with a 2:1 ratio. The Tesla P100 has a higher FP16 peak, but no FP16 benchmark is recorded in the database.

Q: What is the process node and transistor count for each card?

A: The PG506-232 uses a 7 nm TSMC process with 54,200 million transistors on an 826 mm² die. The Tesla P100 uses a 16 nm TSMC process with 15,300 million transistors on a 610 mm² die.

Architecture Differences

The PG506-232 is built on the Ampere architecture with the GA100 chip, while the Tesla P100 is built on the Pascal architecture with the GP100 chip. These are two generations apart in NVIDIA’s server lineup. The PG506-232 belongs to the Server Ampere generation, and the Tesla P100 belongs to the Tesla Pascal generation. The process node differs significantly: the PG506-232 uses a 7 nm process from TSMC, while the Tesla P100 uses a 16 nm process from the same foundry. Transistor counts reflect this: the PG506-232 has 54,200 million transistors, while the Tesla P100 has 15,300 million. The die sizes are 826 mm² for the PG506-232 and 610 mm² for the Tesla P100, and the transistor density is 65.6 million per mm² versus 25.1 million per mm².

The memory architecture also differs. Both use HBM2, but the PG506-232 has 24 GB with a 3072-bit bus, while the Tesla P100 has 16 GB with a 4096-bit bus. The PG506-232 achieves 933.1 GB/s bandwidth, and the Tesla P100 achieves 732.2 GB/s. The clock speeds differ: the PG506-232 has a base of 930 MHz, a boost of 1440 MHz, and a memory clock of 1215 MHz, while the Tesla P100 has a base of 1190 MHz, a boost of 1329 MHz, and a memory clock of 715 MHz. The PG506-232 has 224 tensor cores, and the Tesla P100 has none. The shading unit, TMU, and ROP counts are identical at 3584, 224, and 96, respectively.

The PG506-232 runs FP16 at a 1:1 ratio, matching its FP32 rate of 10.32 TFLOPS. The Tesla P100 runs FP16 at a 2:1 ratio, reaching 19.05 TFLOPS against an FP32 rate of 9.526 TFLOPS. The PG506-232 has a pixel rate of 138.2 GPixel/s and a texture rate of 322.6 GTexel/s. The Tesla P100 has a pixel rate of 127.6 GPixel/s and a texture rate of 297.7 GTexel/s. The PG506-232 uses a PCIe 4.0 x16 interface, while the Tesla P100 uses PCIe 3.0 x16. The PG506-232 has no API support listed for DirectX, OpenGL, or Vulkan, while the Tesla P100 lists DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3.

Specification Differences

The two cards differ in several key specifications. The PG506-232 uses the GA100 chip, while the Tesla P100 uses the GP100 chip. The PG506-232 is on a 7 nm process, and the Tesla P100 is on a 16 nm process. The PG506-232 has 54,200 million transistors, and the Tesla P100 has 15,300 million. The die sizes are 826 mm² and 610 mm², respectively. The base clock is 930 MHz for the PG506-232 and 1190 MHz for the Tesla P100. The boost clock is 1440 MHz for the PG506-232 and 1329 MHz for the Tesla P100. The memory clock is 1215 MHz for the PG506-232 and 715 MHz for the Tesla P100.

Memory capacity differs: 24 GB for the PG506-232 versus 16 GB for the Tesla P100. The memory bus width is 3072 bits for the PG506-232 and 4096 bits for the Tesla P100. Memory bandwidth is 933.1 GB/s for the PG506-232 and 732.2 GB/s for the Tesla P100. The PG506-232 has 224 tensor cores, and the Tesla P100 has none. FP32 throughput is 10.32 TFLOPS for the PG506-232 and 9.526 TFLOPS for the Tesla P100. FP16 throughput is 10.32 TFLOPS for the PG506-232 and 19.05 TFLOPS for the Tesla P100. Pixel rate is 138.2 GPixel/s versus 127.6 GPixel/s, and texture rate is 322.6 GTexel/s versus 297.7 GTexel/s.

The TDP is 165 W for the PG506-232 and 250 W for the Tesla P100. The suggested PSU is 450 W for the PG506-232 and 600 W for the Tesla P100. The power connectors are 8-pin EPS for the PG506-232 and 1x 8-pin for the Tesla P100. The bus interface is PCIe 4.0 x16 for the PG506-232 and PCIe 3.0 x16 for the Tesla P100. The PG506-232 has no display outputs, and neither does the Tesla P100. The PG506-232 lists no API support, while the Tesla P100 lists DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3. The release dates differ: the PG506-232 was released in 2021, and the Tesla P100 was released in 2016. The Tesla P100 has a launch MSRP of 5,699 USD. The PG506-232 lists no launch MSRP. Both cards are dual-slot, and both are 267 mm long. The PG506-232 is 112 mm tall, while the Tesla P100 does not list a height. The Tesla P100 has a predecessor of Tesla Maxwell and a successor of Tesla Volta, while the PG506-232 has a predecessor of Tesla Turing and a successor of Server Ada.