AMD Instinct MI100 vs NVIDIA PG506-232 Comparison

The NVIDIA PG506-232 and AMD Instinct MI100 are both end-of-life server accelerators aimed at compute workloads, but the benchmark data shows they occupy different performance tiers. In the single available OpenCL benchmark, the NVIDIA PG506-232 scores 225124, while the AMD Instinct MI100 scores 139035, giving NVIDIA a 61.9% lead. That gap places the PG506-232 in the 99th percentile of all GPUs, whereas the MI100 sits in the 96th percentile. The PG506-232 outperforms the MI100 by a wide margin, though the MI100’s nearest rivals are all within 2.4% of its score, suggesting it competes in a tighter field.

Where Each One Wins

The NVIDIA PG506-232 wins the only head-to-head benchmark available. In Geekbench OpenCL, it delivers 225124 points against the MI100’s 139035, a delta of 61.9%. This is not a marginal victory; it is a decisive gap that positions the PG506-232 well above the MI100 in raw compute throughput as measured by OpenCL. The PG506-232’s score also sits 8.7% above the NVIDIA A100 PCIe 80 GB (207124) and 14.9% above the NVIDIA RTX 6000D (195964), reinforcing that it is a top-tier performer among NVIDIA’s own lineup. Meanwhile, the MI100’s 139035 score is nearly identical to the NVIDIA Tesla V100 PCIe 16 GB (138063) and only 0.9% higher than the Tesla V100 SXM2 32 GB (137731). In practical terms, the PG506-232 is the clear winner for any workload that relies on OpenCL performance, such as general-purpose GPU computing, scientific simulations, or data processing tasks that leverage OpenCL kernels.

The AMD Instinct MI100 does not win any benchmark in this comparison. However, its loss is contextualized by its nearest rivals: it is only 0.7% ahead of the Tesla V100 PCIe 16 GB and 1.9% ahead of the AMD Radeon PRO V620 (136472). This suggests that while the MI100 trails the PG506-232 significantly, it is still competitive with older NVIDIA Volta-based accelerators and some AMD Radeon PRO cards. For users constrained to AMD’s ecosystem or those requiring specific CDNA features, the MI100 may still be viable, but the data does not show any scenario where it outperforms the PG506-232.

Architecture Differences

The two accelerators diverge sharply in their underlying designs. The NVIDIA PG506-232 uses the GA100 chip built on TSMC’s 7 nm process, packing 54,200 million transistors on a 826 mm² die. This yields a transistor density of 65.6 million transistors per square millimeter. The AMD Instinct MI100 uses the Arcturus chip, also on TSMC’s 7 nm process, but with only 25,600 million transistors on a 750 mm² die, resulting in a density of 34.1 million per square millimeter. NVIDIA’s chip is more than twice as dense, which correlates with its higher compute throughput per unit area.

Memory configurations differ as well. The PG506-232 comes with 24 GB of HBM2 across a 3072-bit bus, delivering 933.1 GB/s of bandwidth. The MI100 offers 32 GB of HBM2 on a wider 4096-bit bus, achieving 1.23 TB/s of bandwidth. Despite having more memory and higher bandwidth, the MI100’s compute performance still lags, indicating that raw memory bandwidth is not the limiting factor in this comparison. Clock speeds are similar: the PG506-232 runs at 930 MHz base and 1440 MHz boost, while the MI100 runs at 1000 MHz base and 1502 MHz boost. The MI100 has a slight clock advantage, but it does not translate into a benchmark win.

Compute resources tell the story. The PG506-232 has 3584 shading units, 224 texture mapping units, and 96 raster operation units, along with 224 tensor cores. Its FP32 throughput is 10.32 TFLOPS, and FP16 is also 10.32 TFLOPS (1:1 ratio). The MI100 has 7680 shading units, 480 TMUs, and 64 ROPs, with no tensor cores. Its FP32 output is 23.07 TFLOPS, and FP16 is 46.14 TFLOPS (2:1 ratio). The MI100’s raw shader count and FP32/FP16 figures are far higher than the PG506-232’s, yet the OpenCL benchmark favors NVIDIA. This suggests that the PG506-232’s tensor cores, which are absent on the MI100, may be heavily leveraged in the OpenCL test, or that NVIDIA’s driver and architecture translate theoretical FLOPs into real-world performance more efficiently.

Power and physical characteristics also differ. The PG506-232 is rated at 165 W TDP with a suggested PSU of 450 W, using a single 8-pin EPS connector. The MI100 draws 300 W TDP with a suggested PSU of 700 W and requires two 8-pin connectors. Both are dual-slot cards with no display outputs, measuring 267 mm in length (10.5 inches) and roughly 112 mm in height (4.4 inches for NVIDIA, 111 mm for AMD). The PG506-232 is significantly more power-efficient, delivering its benchmark lead at nearly half the power draw.

Head-to-Head Benchmarks

The only head-to-head benchmark is Geekbench OpenCL. The NVIDIA PG506-232 scores 225124, while the AMD Instinct MI100 scores 139035. The delta is 61.9% in NVIDIA’s favor, making this a dominant win. To put that in perspective, the PG506-232’s score is 8.7% higher than the NVIDIA A100 PCIe 80 GB (207124) and 14.9% higher than the NVIDIA RTX 6000D (195964). It is 10.4% lower than the NVIDIA L20 (251147), but that card is not part of this comparison. The MI100’s 139035 score is nearly identical to the Tesla V100 PCIe 16 GB (138063), just 0.7% higher, and 0.9% higher than the Tesla V100 SXM2 32 GB (137731). It is also 1.9% above the AMD Radeon PRO V620 (136472) and 2.4% above the AMD Radeon Pro W6800X Duo (135774). Thus, while the PG506-232 competes with modern high-end accelerators, the MI100 sits alongside older Volta-era parts.

The 61.9% delta means that for any OpenCL-bound task, the PG506-232 will finish roughly 60% faster than the MI100, assuming the workload scales linearly with score. This is a substantial advantage that would be visible in real-world compute jobs, from machine learning inference to physics simulations. Conversely, the MI100’s 32 GB memory and higher bandwidth might benefit memory-bound workloads, but the OpenCL score does not reflect any such advantage, and no other benchmark data is available to support that claim.

FAQ

Q: Which GPU has a higher OpenCL benchmark score?

A: The NVIDIA PG506-232 scores 225124, which is 61.9% higher than the AMD Instinct MI100’s 139035.

Q: How does the PG506-232 compare to the NVIDIA A100 PCIe 80 GB?

A: The PG506-232 scores 8.7% higher than the A100 PCIe 80 GB, with scores of 225124 and 207124, respectively.

Q: Is the MI100 competitive with any NVIDIA accelerators?

A: Yes, the MI100’s 139035 score is 0.7% higher than the Tesla V100 PCIe 16 GB (138063) and 0.9% higher than the Tesla V100 SXM2 32 GB (137731).

Q: What are the memory sizes and bandwidths of these two GPUs?

A: The PG506-232 has 24 GB of HBM2 with 933.1 GB/s bandwidth, while the MI100 has 32 GB of HBM2 with 1.23 TB/s bandwidth.

Q: Which GPU has tensor cores?

A: The NVIDIA PG506-232 includes 224 tensor cores; the AMD Instinct MI100 has no tensor cores.

Q: What is the power draw difference?

A: The PG506-232 has a 165 W TDP, while the MI100 has a 300 W TDP. The PG506-232 also recommends a 450 W PSU, versus 700 W for the MI100.

The Verdict

Based strictly on the benchmark data, the NVIDIA PG506-232 is the superior performer. Its OpenCL score of 225124 crushes the MI100’s 139035 by 61.9%, and it sits in the 99th percentile of all GPUs compared to the MI100’s 96th. The PG506-232 also beats two of its nearest rivals, the A100 PCIe 80 GB and RTX 6000D, by 8.7% and 14.9% respectively, confirming its high-end placement. The MI100, by contrast, scores nearly identically to the Tesla V100 series, with deltas under 1%, indicating that it is effectively a peer of older Volta hardware rather than a modern flagship.

For users prioritizing raw OpenCL performance, the PG506-232 is the clear choice. It achieves this at a 165 W TDP versus the MI100’s 300 W, making it more power-efficient as well. The MI100 does offer more memory (32 GB vs 24 GB) and higher bandwidth (1.23 TB/s vs 933.1 GB/s), which could benefit memory-bound workloads, but the available data does not show any benchmark where that translates into a win. The MI100 also has higher theoretical FP32 and FP16 throughput, but the OpenCL result contradicts those specifications, suggesting that real-world performance is dictated by factors beyond raw FLOPs.

The verdict is straightforward: pick the NVIDIA PG506-232 for general compute tasks where OpenCL performance matters. Pick the AMD Instinct MI100 only if the larger memory pool or AMD-specific software stack is an absolute requirement, as the benchmark evidence offers no performance justification for choosing it over the PG506-232. The PG506-232 wins the only head-to-head test, holds a higher percentile ranking, and delivers that performance at lower power consumption.