NVIDIA A100 PCIe 80 GB vs NVIDIA PG506-232 Comparison

The NVIDIA PG506-232 and the NVIDIA A100 PCIe 80 GB are both Ampere-architecture server accelerators built on the GA100 chip, but they serve distinctly different roles in the data center. The benchmark data shows the PG506-232 holding a clear performance edge in the available OpenCL test, while the A100 counters with a vastly larger memory subsystem and higher compute throughput. This analysis breaks down the head-to-head results, architectural choices, and the practical implications for workload selection.

Head-to-Head Benchmarks

The only direct comparison available is the Geekbench OpenCL test, and the result is decisive. The NVIDIA PG506-232 scores 225124, while the NVIDIA A100 PCIe 80 GB scores 207124. That places the PG506-232 8.7% ahead of the A100 in this specific workload. This is a meaningful margin, not a statistical tie. The deltaPct from the A100’s perspective confirms the same figure: the PG506-232 is 8% faster than the A100 when the A100’s nearestRivals list is used as the reference frame.

Context from the rival lists reinforces this. The PG506-232’s 225124 score sits 2.4% above the AMD Radeon PRO W7900D (219827) and 14.9% above the NVIDIA RTX 6000D (195964). It trails the NVIDIA L20 (251147) by 10.4%. The A100’s 207124 score, meanwhile, is 5.7% above the RTX 6000D (195964) and 6.5% above the NVIDIA Tesla V100S PCIe 32 GB (194415), but it falls 5.8% short of the Radeon PRO W7900D. The head-to-head delta is the largest single gap in either card’s immediate rival set, which suggests the PG506-232’s advantage over the A100 is not an outlier but a consistent performance relationship.

Why does the PG506-232 win? The benchmark result reflects more than just raw specs. The PG506-232 operates with a boost clock of 1440 MHz, which is 30 MHz higher than the A100’s 1410 MHz boost. Its base clock is lower at 930 MHz versus 1065 MHz, but the sustained boost behavior appears to favor the PG506-232 in this OpenCL test. The A100’s higher shading unit count (6912 versus 3584) does not translate into a win here, indicating that the OpenCL workload may be sensitive to clock speed, memory latency, or driver scheduling rather than pure parallel throughput.

The wins tally is straightforward: the PG506-232 takes 1 win out of 1 head-to-head benchmark, while the A100 takes 0. No other benchmark tests are available in the data, so this single result stands as the only direct performance comparison. For workloads that resemble this OpenCL test, the PG506-232 is the faster card by a nearly 9% margin.

Architecture Differences

Both cards share the same foundational silicon: the GA100 chip, built on a 7 nm process at TSMC, with 54,200 million transistors on an 826 mm² die. The transistor density is identical at 65.6M per mm². Both belong to the Server Ampere (Axx) generation, use the PCIe 4.0 x16 bus interface, and have no display outputs. The production status is end-of-life for both, with the same predecessor (Tesla Turing) and successor (Server Ada).

The divergence begins with the memory subsystem. The PG506-232 uses 24 GB of HBM2 with a 3072-bit bus width, delivering 933.1 GB/s of bandwidth. The A100 PCIe 80 GB uses 80 GB of HBM2e with a 5120-bit bus width, delivering 1.94 TB/s. That is over twice the memory capacity and more than double the bandwidth. The memory clock also differs: the PG506-232 runs at 1215 MHz (2.4 Gbps effective), while the A100 runs at 1512 MHz (3 Gbps effective). The A100’s memory advantage is the single largest architectural gap between the two cards.

Compute resources follow a similar pattern. The A100 has 6912 shading units, 432 TMUs, 160 ROPs, and 432 tensor cores. The PG506-232 has 3584 shading units, 224 TMUs, 96 ROPs, and 224 tensor cores. In every compute category, the A100 has exactly twice the resources of the PG506-232, or close to it. The FP32 throughput reflects this: the A100 delivers 19.49 TFLOPS versus the PG506-232’s 10.32 TFLOPS. The FP16 numbers are even more lopsided: the A100 reaches 77.97 TFLOPS (at a 4:1 ratio), while the PG506-232 manages 10.32 TFLOPS (at a 1:1 ratio). The A100’s FP16 capability is 7.5 times higher, assuming the ratio differences are handled correctly.

Pixel and texture rates also favor the A100: 225.6 GPixel/s and 609.1 GTexel/s versus the PG506-232’s 138.2 GPixel/s and 322.6 GTexel/s. The A100’s boost clock is slightly lower (1410 MHz versus 1440 MHz), but the extra execution units more than compensate. The PG506-232’s only clock advantage is the boost frequency; its base clock is 135 MHz lower.

Physical specifications are nearly identical. Both are dual-slot cards with an 8-pin EPS power connector and no display outputs. The PG506-232 measures 267 mm in length and 112 mm in height; the A100 measures 267 mm in length and 111 mm in height — a 1 mm difference. Power draws differ substantially: the PG506-232 has a 165 W TDP with a suggested PSU of 450 W, while the A100 has a 300 W TDP with a suggested PSU of 700 W.

FAQ

Q: Which card is faster in the available benchmark?

A: The NVIDIA PG506-232 scores 225124 in Geekbench OpenCL, which is 8.7% higher than the A100 PCIe 80 GB’s 207124 score. The PG506-232 wins the only head-to-head test available.

Q: Does the A100 PCIe 80 GB have more memory bandwidth?

A: Yes. The A100 uses 80 GB of HBM2e with a 5120-bit bus and 1.94 TB/s bandwidth. The PG506-232 has 24 GB of HBM2 with a 3072-bit bus and 933.1 GB/s bandwidth.

Q: What is the FP32 compute difference?

A: The A100 delivers 19.49 TFLOPS FP32, while the PG506-232 delivers 10.32 TFLOPS. The A100 has nearly double the FP32 throughput.

Q: Are both cards based on the same chip?

A: Yes, both use the GA100 chip on TSMC’s 7 nm process. They share the same transistor count of 54,200 million and the same die size of 826 mm².

Q: What is the TDP difference?

A: The PG506-232 has a 165 W TDP with a suggested PSU of 450 W. The A100 has a 300 W TDP with a suggested PSU of 700 W.

Q: Do either cards have display outputs?

A: No. Both the PG506-232 and the A100 PCIe 80 GB have no display outputs, consistent with their server accelerator roles.

Specification Differences

The following fields differ between the two cards:

• Base clock: PG506-232 at 930 MHz; A100 at 1065 MHz.
• Boost clock: PG506-232 at 1440 MHz; A100 at 1410 MHz.
• Memory size: PG506-232 at 24 GB; A100 at 80 GB.
• Memory type: PG506-232 uses HBM2; A100 uses HBM2e.
• Memory bus width: PG506-232 at 3072 bit; A100 at 5120 bit.
• Memory bandwidth: PG506-232 at 933.1 GB/s; A100 at 1.94 TB/s.
• Memory clock: PG506-232 at 1215 MHz (2.4 Gbps effective); A100 at 1512 MHz (3 Gbps effective).
• Shading units: PG506-232 at 3584; A100 at 6912.
• TMUs: PG506-232 at 224; A100 at 432.
• ROPs: PG506-232 at 96; A100 at 160.
• Tensor cores: PG506-232 at 224; A100 at 432.
• Pixel rate: PG506-232 at 138.2 GPixel/s; A100 at 225.6 GPixel/s.
• Texture rate: PG506-232 at 322.6 GTexel/s; A100 at 609.1 GTexel/s.
• FP32: PG506-232 at 10.32 TFLOPS; A100 at 19.49 TFLOPS.
• FP16: PG506-232 at 10.32 TFLOPS (1:1); A100 at 77.97 TFLOPS (4:1).
• TDP: PG506-232 at 165 W; A100 at 300 W.
• Suggested PSU: PG506-232 at 450 W; A100 at 700 W.
• Height: PG506-232 at 112 mm; A100 at 111 mm.
• Release date: PG506-232 on 2021-04-11; A100 on 2021-06-27.

Identical fields include the chip (GA100), architecture (Ampere), process node (7 nm), foundry (TSMC), transistor count (54,200 million), die size (826 mm²), transistor density (65.6M / mm²), bus interface (PCIe 4.0 x16), slot width (dual-slot), power connector (8-pin EPS), length (267 mm), display outputs (none), production status (end-of-life), generation (Server Ampere), predecessor (Tesla Turing), and successor (Server Ada).

Where Each One Wins

The PG506-232 wins in the only direct benchmark comparison. Its Geekbench OpenCL score of 225124 beats the A100’s 207124 by 8.7%. This makes it the better choice for workloads that mirror that specific OpenCL test — likely compute tasks that respond to higher boost clocks (1440 MHz versus 1410 MHz) and lower power overhead. The PG506-232 also wins on efficiency: its 165 W TDP is roughly half the A100’s 300 W TDP, and its suggested PSU of 450 W versus 700 W means it fits into more modest server power envelopes. For single-slot or power-constrained deployments where the OpenCL-style workload dominates, the PG506-232 is the faster and more efficient card.

The A100 PCIe 80 GB wins on nearly every raw compute specification. It has double the shading units (6912 versus 3584), double the TMUs (432 versus 224), and 67% more ROPs (160 versus 96). Its FP32 throughput of 19.49 TFLOPS is 89% higher than the PG506-232’s 10.32 TFLOPS. The FP16 difference is even starker: 77.97 TFLOPS versus 10.32 TFLOPS. The A100 also wins decisively on memory. Its 80 GB capacity is 3.3 times larger, and its 1.94 TB/s bandwidth is more than double the PG506-232’s 933.1 GB/s. For large model training, high-resolution inference, or any workload that exceeds 24 GB of working set, the A100 is the only viable option between the two.

The A100 also wins on texture and pixel throughput, with 609.1 GTexel/s versus 322.6 GTexel/s and 225.6 GPixel/s versus 138.2 GPixel/s. Those numbers matter less for pure compute but could influence mixed workloads that involve image processing or volume rendering. The A100 has more tensor cores (432 versus 224), which is critical for transformer-based AI workloads, even if the OpenCL benchmark does not capture that advantage.

The Verdict

The data presents a clear trade-off. The PG506-232 wins the available performance test, delivering 8.7% higher OpenCL scores than the A100 PCIe 80 GB. It also consumes less power (165 W versus 300 W) and requires a smaller PSU (450 W versus 700 W). For users whose workload matches the Geekbench OpenCL profile and fits within the 24 GB memory limit, the PG506-232 is the faster choice — and the benchmark result is the only direct performance evidence available.

The A100 PCIe 80 GB, however, is the superior accelerator for memory-bound and high-throughput compute. Its 80 GB HBM2e memory and 1.94 TB/s bandwidth are unmatched by the PG506-232. Its FP32 and FP16 compute rates are dramatically higher, and its tensor core count is double. The A100 loses the single benchmark but wins every specification that matters for large-scale AI training, scientific simulation, and data-intensive inference.

The verdict splits along workload lines. If the task is a single OpenCL-style compute job with modest memory requirements, the PG506-232 offers better performance per watt and a faster benchmark result. If the task involves large models, massive datasets, or FP16-accelerated training, the A100 PCIe 80 GB is the only rational pick — its memory capacity and bandwidth are decisive. Both cards are end-of-life, so the choice is about existing deployments rather than new purchases. In that context, the PG506-232 is the benchmark winner, but the A100 is the compute heavy-weight.