NVIDIA B200 vs NVIDIA PG506-232 Comparison

The NVIDIA B200 and NVIDIA PG506-232 represent two distinct generations of server computing, and the benchmark data shows a decisive performance gap. The B200, built on the Blackwell architecture, delivers a Geekbench OpenCL score of 345,482, while the older PG506-232, based on Ampere, scores 225,124. This translates to a 53.5% advantage for the B200 in the sole head-to-head test, a substantial margin that underscores the generational leap in compute capability. The data suggests the B200 is not merely an incremental upgrade but a complete redefinition of server-grade performance.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, and the results are unambiguous. The NVIDIA B200 scores 345,482 points, while the NVIDIA PG506-232 scores 225,124 points. The B200’s victory is defined by a delta of 53.5%, meaning it outperforms the PG506-232 by more than half its own score. This is not a close contest; it is a dominant showing that indicates the B200 handles compute workloads with significantly greater efficiency and raw power.

To contextualize the B200’s performance, it sits at the 100th percentile of all GPUs, while the PG506-232 is at the 99th percentile. This means the B200 is at the absolute top of the database, whereas the PG506-232 is just below the pinnacle. The B200’s nearest rivals in the data include the NVIDIA B300 SXM6 AC, which scores 369,831 and is 6.6% ahead, and the NVIDIA H200 NVL, which scores 334,891 and is 3.2% behind. The PG506-232’s closest competitor is the NVIDIA L20, which scores 251,147 and is 10.4% ahead, showing that even within its own generation, the PG506-232 is challenged by newer or differently positioned parts.

The delta between the two is stark. The PG506-232’s score of 225,124 is comparable to the AMD Radeon PRO W7900D, which scores 219,827 and is 2.4% behind. In contrast, the B200’s score of 345,482 is closer to the B300 SXM6 AC, which is 6.6% faster, and the H200 NVL, which is 3.2% slower. This positioning indicates that the B200 competes at a performance tier far above the PG506-232, which is more aligned with mid-range workstation and server accelerators of its era. The 53.5% delta is a clear signal that anyone migrating from the PG506-232 to the B200 would experience a profound uplift in compute throughput.

Architecture Differences

The architectural divide between these two GPUs is vast, starting with the manufacturing process. The B200 uses a 5 nm process at TSMC, while the PG506-232 uses a 7 nm process, also at TSMC. This node advantage allows the B200 to pack 104,000 million transistors onto its die, compared to the PG506-232’s 54,200 million. The PG506-232 does have a documented die size of 826 mm² and a transistor density of 65.6M / mm², but the B200’s transistor count is nearly double, even without a listed die size.

The chip architectures reinforce this gap. The B200 is based on the GB100 chip under the Blackwell architecture, while the PG506-232 uses the GA100 chip under Ampere. This generational shift brings significant changes to core configurations. The B200 features 18,944 shading units, 592 TMUs, and 24 ROPs, whereas the PG506-232 has 3,584 shading units, 224 TMUs, and 96 ROPs. The B200 has dramatically more shading units and TMUs, though the PG506-232 has more ROPs, which suggests a different balance between pixel processing and general compute.

Memory is another critical differentiator. The B200 comes with 90 GB of HBM3e memory on a 4096-bit bus, delivering 4.10 TB/s of bandwidth. The PG506-232 is equipped with 24 GB of HBM2 memory on a 3072-bit bus, providing 933.1 GB/s of bandwidth. The B200’s memory bandwidth is over four times higher, which is crucial for data-intensive workloads. The memory clock speeds also differ: the B200 runs at 2000 MHz (8 Gbps effective), while the PG506-232 runs at 1215 MHz (2.4 Gbps effective). The B200’s tensor core count is 592, compared to the PG506-232’s 224, indicating a massive boost in AI and deep learning capabilities.

Clock speeds tell a nuanced story. The B200 has a lower base clock of 700 MHz but a higher boost clock of 1965 MHz, while the PG506-232 has a base clock of 930 MHz and a boost of 1440 MHz. This suggests the B200 can ramp up to higher frequencies under load, despite a lower idle state. The power and physical specifications also diverge: the B200 has a TDP of 1000 W and is an SXM Module, while the PG506-232 has a TDP of 165 W and is a dual-slot card. The B200 requires a 1400 W suggested PSU, whereas the PG506-232 only needs 450 W, reflecting the B200’s far greater energy draw for its performance.

Where Each One Wins

Based on the data, the NVIDIA B200 wins in every measurable compute category. It is 53.5% faster in Geekbench OpenCL, and its FP32 performance is 74.45 TFLOPS compared to the PG506-232’s 10.32 TFLOPS. This makes the B200 the clear choice for high-performance computing, scientific simulations, and any workload that demands massive parallel processing power. The B200’s FP16 performance is 1,191.2 TFLOPS (16:1), while the PG506-232 offers 10.32 TFLOPS (1:1), showing that the B200 is particularly optimized for mixed-precision AI training, where it has a 115x advantage in raw throughput.

The B200 also wins on memory capacity and bandwidth, with 90 GB and 4.10 TB/s versus the PG506-232’s 24 GB and 933.1 GB/s. This makes the B200 superior for large language models, big data analytics, and other memory-bound tasks. The PG506-232, however, has a lower TDP of 165 W and a smaller physical footprint, making it a more power-efficient option for environments where energy consumption is a constraint, though it lacks the B200’s sheer capability. The PG506-232 also has a higher base clock of 930 MHz, which could offer better performance in lightly threaded tasks that do not utilize the full GPU, but this is speculative and not supported by benchmark data.

The PG506-232’s wins are limited to power efficiency and form factor. It is a dual-slot card with a 8-pin EPS power connector, making it easier to integrate into existing server chassis, while the B200 is an SXM Module that requires specialized infrastructure. The PG506-232 is also end-of-life, while the B200 is active, but that is a production status, not a performance metric. In terms of raw speed, the B200 is the unequivocal winner, and the PG506-232’s advantages are purely operational rather than performance-based.

FAQ

Q: What is the performance difference between the NVIDIA B200 and PG506-232?

A: The B200 scores 345,482 in Geekbench OpenCL, while the PG506-232 scores 225,124. This gives the B200 a 53.5% higher score, making it significantly faster in this benchmark.

Q: How do the memory configurations compare?

A: The B200 has 90 GB of HBM3e memory with a 4096-bit bus and 4.10 TB/s bandwidth. The PG506-232 has 24 GB of HBM2 memory with a 3072-bit bus and 933.1 GB/s bandwidth, meaning the B200 offers over four times the bandwidth.

Q: Which GPU has better FP32 compute performance?

A: The B200 delivers 74.45 TFLOPS of FP32 performance, while the PG506-232 provides 10.32 TFLOPS. The B200 is substantially ahead in this metric.

Q: Are there any architectural differences in transistor count?

A: Yes, the B200 uses a 5 nm process with 104,000 million transistors, while the PG506-232 uses a 7 nm process with 54,200 million transistors. The B200’s die is based on the GB100 chip, compared to the PG506-232’s GA100 chip.

Q: What is the power requirement for each GPU?

A: The B200 has a TDP of 1000 W and requires a suggested PSU of 1400 W. The PG506-232 has a TDP of 165 W and a suggested PSU of 450 W, making the PG506-232 far more power-efficient.

Q: How does the B200 rank against its nearest rivals?

A: The B200 is at the 100th percentile, with the NVIDIA B300 SXM6 AC being 6.6% faster and the NVIDIA H200 NVL being 3.2% slower. The PG506-232 is at the 99th percentile, with the NVIDIA L20 being 10.4% faster.

The Verdict

The data is clear: the NVIDIA B200 is the superior performer for any compute-intensive task. Its 53.5% lead in Geekbench OpenCL over the PG506-232, combined with its massive advantages in FP32 (74.45 TFLOPS vs. 10.32 TFLOPS), FP16 (1,191.2 TFLOPS vs. 10.32 TFLOPS), and memory bandwidth (4.10 TB/s vs. 933.1 GB/s), makes it the definitive choice for AI research, scientific computing, and high-performance data centers. The B200’s 90 GB of memory also allows it to handle larger datasets and models than the PG506-232’s 24 GB, which is critical for modern deep learning workloads.

The PG506-232, however, is not without its merits. With a TDP of just 165 W, it is far more power-efficient, and its dual-slot design with an 8-pin EPS connector makes it easier to deploy in standard server environments. It is also end-of-life, meaning it may be available at lower costs in secondary markets, but the data cannot confirm pricing. For users with legacy applications that do not require extreme compute, the PG506-232 remains a capable accelerator, but it is outclassed by the B200 in every benchmark category.

The verdict is straightforward: choose the B200 if raw performance, memory capacity, and future-proofing are your priorities. Choose the PG506-232 if power constraints and physical form factor are the deciding factors, but be prepared for a significant performance trade-off. The B200 is the benchmark leader, and the PG506-232 is a previous-generation solution that cannot match its speed.

Specification Differences

• Process Node: The B200 uses a 5 nm process, while the PG506-232 uses a 7 nm process.
• Transistors: The B200 has 104,000 million transistors, whereas the PG506-232 has 54,200 million.
• Die Size: The PG506-232 has a die size of 826 mm² and a density of 65.6M / mm²; the B200 does not list these.
• Base Clock: The B200 runs at 700 MHz, while the PG506-232 runs at 930 MHz.
• Boost Clock: The B200 boosts to 1965 MHz, while the PG506-232 boosts to 1440 MHz.
• Memory Clock: The B200 uses 2000 MHz (8 Gbps effective), while the PG506-232 uses 1215 MHz (2.4 Gbps effective).
• Memory Size: The B200 has 90 GB, while the PG506-232 has 24 GB.
• Memory Type: The B200 uses HBM3e, while the PG506-232 uses HBM2.
• Memory Bus: The B200 has a 4096-bit bus, while the PG506-232 has a 3072-bit bus.
• Memory Bandwidth: The B200 has 4.10 TB/s, while the PG506-232 has 933.1 GB/s.
• Shading Units: The B200 has 18,944, while the PG506-232 has 3,584.
• TMUs: The B200 has 592, while the PG506-232 has 224.
• ROPs: The B200 has 24, while the PG506-232 has 96.
• Tensor Cores: The B200 has 592, while the PG506-232 has 224.
• Pixel Rate: The B200 is 47.16 GPixel/s, while the PG506-232 is 138.2 GPixel/s.
• Texture Rate: The B200 is 1,163.3 GTexel/s, while the PG506-232 is 322.6 GTexel/s.
• FP32: The B200 is 74.45 TFLOPS, while the PG506-232 is 10.32 TFLOPS.
• FP16: The B200 is 1,191.2 TFLOPS (16:1), while the PG506-232 is 10.32 TFLOPS (1:1).
• TDP: The B200 is 1000 W, while the PG506-232 is 165 W.
• Slot Width: The B200 is an SXM Module, while the PG506-232 is dual-slot.
• Power Connectors: The PG506-232 uses an 8-pin EPS; the B200 has none listed.
• Suggested PSU: The B200 requires 1400 W, while the PG506-232 requires 450 W.
• Bus Interface: The B200 uses PCIe 5.0 x16, while the PG506-232 uses PCIe 4.0 x16.
• Dimensions: The PG506-232 is 267 mm long and 112 mm high; the B200 has no dimensions listed.
• Production Status: The B200 is Active, while the PG506-232 is End-of-life.
• Release Date: The PG506-232 was released on 2021-04-11; the B200 has no release date listed.
• Chip: The B200 uses GB100, while the PG506-232 uses GA100.
• Architecture: The B200 is Blackwell, while the PG506-232 is Ampere.