NVIDIA PG506-242

The NVIDIA PG506-242 is a server-oriented Ampere-generation accelerator built around the GA100 chip, manufactured on TSMC’s 7 nm process with 54,200 million transistors on a 826 mm² die. It targets compute-heavy workloads rather than consumer graphics, and its benchmark data reflects that positioning: the GPU holds a 50th percentile rank among all GPUs in the database, with an average benchmark score of 0. This places it in a neutral middle ground—neither a flagship nor a budget part—though the absence of specific benchmark scores for the card itself means its competitive standing must be inferred from its architectural specifications and the relative performance of its nearest rivals, which are not listed in the available data.

Benchmark Performance

The PG506-242’s raw computational throughput is defined by its 3,584 shading units, 224 texture mapping units, and 96 raster output units. Its FP32 performance is rated at 10.32 TFLOPS, while FP16 performance is identical at 10.32 TFLOPS (1:1 ratio). This 1:1 FP16/FP32 ratio is notable for a server part, as many accelerators prioritize FP16 or tensor workloads, but here the symmetric throughput suggests a design balanced for general-purpose compute. The pixel rate is 138.2 GPixel/s, and the texture rate is 322.6 GTexel/s, both figures that would support moderate rasterization tasks, though the card lacks display outputs entirely, indicating that rendering is not its primary function.

Without a benchmark score or a nearestRivals list, the percentile rank of 50 is the only direct comparative metric available. That percentile suggests the PG506-242 sits exactly at the midpoint of all GPUs in the database, implying it outperforms roughly half of recorded hardware and underperforms the other half. In practical terms, this means its 10.32 TFLOPS FP32 throughput is competitive with many mid-range workstation and server cards from its generation, but it falls short of high-end Ampere data center parts that would push FP32 counts into the 20+ TFLOPS range. The data shows no deltas to report, so any specific percentage advantage or deficit relative to named rivals cannot be quantified from the FACT PACK. What can be stated is that the PG506-242’s compute density, at 65.6M transistors per mm², is a direct consequence of the 7 nm process and contributes to its 165 W TDP—a modest power envelope for the performance class.

Ray Tracing and Feature Set

The PG506-242 does not list dedicated ray tracing cores in its specifications. The FACT PACK shows null values for rtCores, indicating that hardware-accelerated ray tracing is either absent or not exposed as a separate unit. This is consistent with the GA100 chip’s design philosophy, which focuses on tensor operations and matrix math rather than real-time graphics effects. The card does include 224 tensor cores, which are Ampere-generation units designed for AI inference and training workloads. These tensor cores enable accelerated FP16 and mixed-precision operations, though the FACT PACK provides no separate tensor TFLOPS figure.

In terms of API support, the FACT PACK lists null values for DirectX, OpenGL, and Vulkan. This absence of API data reinforces the server-oriented nature of the PG506-242; it is not intended for gaming or conventional graphics applications where those APIs would be required. Instead, the feature set centers on the tensor cores and the 1:1 FP16 capability, which are well-suited for deep learning, scientific simulations, and data center inference tasks. The lack of display outputs (listed as "No outputs") further confirms that this is a compute accelerator, not a graphics card. Users requiring ray tracing or modern graphics API support would need to look elsewhere, as the PG506-242 provides no such capabilities in the recorded specifications.

How It Compares

The FACT PACK provides no nearestRivals entries, so there are no named competitors with specific score deltas to analyze. However, the percentile rank of 50 offers a general frame of reference. Against a hypothetical mid-range Ampere server GPU, the PG506-242’s 10.32 TFLOPS FP32 would likely be on par, while a high-end GA100 derivative (such as the A100) would exceed it in both compute and memory bandwidth. The PG506-242’s 24 GB HBM2 memory and 933.1 GB/s bandwidth are substantial, but without rival specs, the comparison must remain qualitative. The data shows that the card occupies a middle tier: it is not a flagship, but it is not a low-end part either. Its 165 W TDP is low for the memory capacity and compute throughput, which could make it attractive in power-constrained server environments, but again, no rival power figures are available for direct comparison.

The absence of benchmark scores and rival data means the PG506-242’s position is defined by its architecture and feature set rather than measured performance. Its 50th percentile rank suggests it is neither a standout nor a laggard, and the lack of ray tracing cores and display outputs clearly separates it from consumer GPUs. For a database user, this card would be evaluated on its compute capabilities, tensor core count, and memory subsystem, not on gaming or graphics performance.

Who Should Consider It

Given the compute-oriented specifications, the PG506-242 is best suited for workloads that leverage its FP16 and tensor core capabilities. The 10.32 TFLOPS FP16 throughput, identical to FP32, indicates strong performance for mixed-precision training or inference tasks where FP16 is used to accelerate matrix operations. The 24 GB HBM2 memory with 933.1 GB/s bandwidth provides ample capacity for large models or datasets, and the 3072-bit bus width ensures high throughput for memory-bound operations. For resolution and settings-based gaming recommendations, the data provides no basis—there are no display outputs, no DirectX support, and no rasterization benchmarks. Therefore, the card is not recommended for any gaming scenario, regardless of resolution or graphical settings.

For server deployments, the PG506-242 could be considered for AI inference at scale, particularly where power efficiency is a priority (165 W TDP). The 224 tensor cores would accelerate transformer-based models or convolutional networks, and the 1:1 FP16 ratio suggests that the card does not artificially limit half-precision throughput, which is a common optimization in consumer parts. The 50th percentile ranking implies that it would handle mid-sized compute tasks competently but might struggle with the largest models that require more than 24 GB of VRAM or higher tensor throughput. Users with workloads that fit within the memory capacity and that benefit from FP16 acceleration would find this card viable, while those needing FP64 or ray tracing would not.

Power and Cooling

The PG506-242 has a TDP of 165 W, which is a low figure for a GPU with 24 GB of HBM2 memory and a 54,200 million-transistor die. The suggested PSU is 450 W, indicating that the card requires a modest power supply relative to its compute capabilities. The power connector is an 8-pin EPS, a server-standard connector rather than the PCIe 8-pin found on consumer cards, which means installation requires a motherboard or power distribution board that supports EPS outputs. The cooling solution is listed as dual-slot, with physical dimensions of 267 mm in length (10.5 inches) and 112 mm in height (4.4 inches). This form factor is typical for server accelerators, allowing for passive or active cooling depending on chassis airflow.

The 165 W TDP is a key selling point for dense server deployments, as it permits higher GPU-to-power ratios compared to higher-wattage accelerators. However, the lack of display outputs means that the card cannot be used for any video output, and the 8-pin EPS connector may require adapter cables in systems designed for standard PCIe power. The production status is end-of-life, with a release date of April 11, 2021, and its predecessor is Tesla Turing while its successor is Server Ada, placing it in the first wave of Ampere server parts. Users building new systems would likely not choose this card due to its end-of-life status, but existing deployments could still benefit from its power efficiency.

Memory Subsystem

The PG506-242 is equipped with 24 GB of HBM2 memory, connected via a 3072-bit bus. The memory clock is 1215 MHz, translating to 2.4 Gbps effective data rate, and the resulting bandwidth is 933.1 GB/s. This is a substantial memory pipeline, particularly when compared to GDDR6-based cards of similar era, which typically offer bus widths of 256 or 384 bits and bandwidths in the 400–600 GB/s range. The 3072-bit bus is a hallmark of HBM2 implementations, which sacrifice clock speed for width, and the 933.1 GB/s bandwidth ensures that the 3584 shading units and 224 tensor cores are not starved for data.

For high-resolution workloads, such as training on large image datasets or processing 3D volumetric data, the 24 GB capacity allows entire models to reside in VRAM, avoiding costly host-device transfers. The bandwidth of 933.1 GB/s supports rapid iteration over this data, which is critical for convergence in deep learning. However, the card’s lack of display outputs means that "high resolution" in a gaming context is irrelevant; instead, high resolution refers to the resolution of the data being processed, such as 4K or 8K textures in a render farm, though the card has no graphics API support. The memory subsystem is clearly designed for throughput, not latency optimization, and the 1:1 FP16 ratio pairs well with the bandwidth to enable efficient half-precision training loops. The 24 GB capacity also exceeds many consumer cards of its generation, making it suitable for models that would otherwise require multi-GPU sharding.