NVIDIA PG506-217

Memory Subsystem, VRAM size/type, bus width, bandwidth and what it means for high resolutions

The PG506-217 carries 24 GB of HBM2 memory, a configuration that immediately signals server-grade intent rather than consumer gaming focus. The 3072-bit bus width is exceptionally wide, which compensates for the relatively modest 2.4 Gbps effective memory clock by delivering a total bandwidth of 933.1 GB/s. This is a critical figure: at 4K resolutions, texture streaming and frame buffer pressure increase disproportionately, and a bandwidth figure near 1 TB/s ensures that the GA100 chip’s compute units are not starved by memory latency or throughput limits.

What does this mean in practice? For high-resolution workloads, the data implies that the PG506-217 can sustain large working sets without spilling to slower storage. The 24 GB capacity alone can hold multiple 4K render layers, large AI inference batches, or substantial simulation meshes. The HBM2 type further reduces power consumption compared to GDDR6 alternatives, though the architecture’s true strength lies in the aggregate bandwidth across the 3072-bit interface. The 933.1 GB/s figure is roughly three to four times what typical consumer GPUs of the same era offered, making this a memory-bound problem solver.

At 1440p and below, the bandwidth advantage becomes less critical, but the capacity still matters for multi-tasking or running several models simultaneously. The 24 GB pool is a meaningful threshold: it allows datasets that would otherwise be split across multiple devices to reside on a single card. For 8K or multi-display environments, the bandwidth and capacity together suggest headroom for extreme resolutions, though the card’s lack of display outputs (noted elsewhere) means this capability is for compute or rendering, not direct visual output.

Ray Tracing and Feature Set, RT/tensor cores, API support from facts

The PG506-217 does not list dedicated ray tracing cores. The the benchmark database shows `rtCores: null`, which is a notable absence for an Ampere-generation product, as many consumer Ampere cards include dedicated RT hardware. Instead, the card relies on 224 tensor cores for AI-accelerated workloads. These tensor cores are the defining feature here: they enable matrix math acceleration for deep learning, inference, and certain scientific computing tasks. The tensor core count of 224 is substantial, suggesting that the PG506-217 is optimized for neural network training and inference rather than real-time ray-traced graphics.

API support is sparse: DirectX, OpenGL, and Vulkan fields are all null. This reinforces the card’s positioning as a compute accelerator rather than a graphics adapter. Without these APIs, traditional gaming or interactive rendering workloads are not the intended use case. The 7 nm TSMC process and 54,200 million transistors on an 826 mm² die indicate a dense, compute-first design. The absence of RT cores means ray tracing workloads would need to be handled via compute shaders or tensor-core-based denoising, but the hardware itself is not optimized for that path.

The feature set points to a server-oriented accelerator: tensor cores for AI, HBM2 for bandwidth, and no display outputs. This is a card that processes data, not pixels for a monitor. The 224 tensor cores, combined with the 1:1 FP16 to FP32 ratio (both at 10.32 TFLOPS), suggest balanced performance for mixed-precision workloads. The lack of RT cores is a deliberate trade-off: resources are allocated to tensor throughput rather than ray intersection hardware.

Benchmark Performance, analyze scores vs rivals with exact % deltas

The the benchmark database lists `benchmarks: []` and `nearestRivals: []`, meaning there are no direct comparison scores or percentile deltas available from the provided data. The `avgBenchmarkScore` is 0, and the `percentileVsAllGpus` is 50, placing it exactly at the median of all GPUs in the database. This is a curious position: a server card with 10.32 TFLOPS FP32 and 933.1 GB/s bandwidth sits at the 50th percentile, which suggests that the database’s GPU population includes many consumer and professional parts with higher raw graphics scores.

Without rival names or delta percentages, the interpretation must rely on the raw compute metrics. The 10.32 TFLOPS FP32 rate is modest compared to high-end consumer cards of the same era, which often exceeded 20 TFLOPS. However, the 1:1 FP16 ratio doubles the effective throughput for mixed-precision tasks, making it competitive for AI workloads. The texture rate of 322.6 GTexel/s and pixel rate of 138.2 GPixel/s are decent but not class-leading, reflecting the 3584 shading units and 224 TMUs.

The 50th percentile ranking implies that half of the GPUs in the database outperform it in aggregate benchmark scores. This is expected for a compute-focused card that lacks graphics-oriented features. The data cannot confirm specific rival deltas, so any comparison must be qualitative: the PG506-217 trades graphics flexibility for memory bandwidth and tensor throughput. Its performance in compute benchmarks would likely be strong, but the the benchmark database provides no direct evidence to quantify that advantage.

Power and Cooling, TDP, PSU recommendation, connector requirements

The PG506-217 has a TDP of 165 W, which is remarkably low for a card with 24 GB HBM2 and a 54,200-million-transistor die. This efficiency stems from the 7 nm TSMC process and the server-oriented design that prioritizes performance per watt. The suggested PSU is 450 W, leaving ample headroom for a system with a modest CPU and peripherals. The power connector is an 8-pin EPS, which is a server-standard connector rather than the 8-pin PCIe found on consumer cards. This is an important caveat for any potential installation: the physical connector differs, and adapters may not be readily available.

Cooling is handled by a dual-slot design, which is typical for server accelerators. The 267 mm length (10.5 inches) and 112 mm height (4.4 inches) are standard for a dual-slot card, but the lack of display outputs means it is intended for chassis with directed airflow rather than open test benches. The 165 W TDP suggests that a capable air cooler is sufficient, and the dual-slot form factor allows for dense server configurations.

The 450 W PSU recommendation is conservative, indicating that the card draws minimal power under load. However, the 8-pin EPS connector may require a power supply with server-grade cabling. Most consumer PSUs do not include EPS connectors for GPUs, so a custom adapter or a server PSU is likely necessary. The low TDP is a double-edged sword: it enables high-density deployment but also signals that the card is not designed for peak burst performance.

Who Should Consider It, resolution/settings-based recommendations grounded in the scores

The PG506-217 is not a gaming card, and the data makes this clear: no RT cores, no display outputs, and null graphics APIs. However, for compute workloads, the 24 GB HBM2 and 933.1 GB/s bandwidth make it a strong candidate for AI inference, scientific simulation, or data processing tasks that require large memory pools. The 10.32 TFLOPS FP32 and 10.32 TFLOPS FP16 (1:1) scores indicate balanced performance for mixed-precision training, where the tensor cores can accelerate matrix operations.

At high resolutions in a rendering context, the card’s memory capacity is the primary asset. A 4K render farm could benefit from 24 GB per card, allowing complex scenes to fit in memory without tiling. The bandwidth of 933.1 GB/s ensures that texture streaming and geometry loads do not bottleneck. For 1440p or 1080p workloads, the card is overkill in memory but underpowered in shading units compared to dedicated gaming GPUs, the 3584 shading units are fewer than many consumer cards.

Given the 50th percentile ranking, users should not expect top-tier raw performance. Instead, the value lies in the memory subsystem and tensor cores. This card is for researchers, engineers, or data scientists who need HBM2 bandwidth and large capacity without the cost or power draw of a full server GPU. It is not for gamers, and the lack of display outputs means it must be paired with a secondary GPU for any visual output.

FAQ

Q: What is the memory configuration of the PG506-217?

A: It has 24 GB of HBM2 memory with a 3072-bit bus width and 933.1 GB/s bandwidth.

Q: Does the PG506-217 support ray tracing?

A: No dedicated ray tracing cores are listed; the card relies on 224 tensor cores for AI acceleration.

Q: What is the power consumption and PSU requirement?

A: The TDP is 165 W, and the suggested PSU is 450 W, using an 8-pin EPS connector.

Q: What is the FP32 and FP16 performance?

A: Both are 10.32 TFLOPS, with a 1:1 ratio for mixed-precision workloads.

Q: Does the card have display outputs?

A: No, it has no display outputs, making it unsuitable for direct monitor connection.

Q: What is the card’s production status?

A: It is end-of-life, released on 2021-04-11.

How It Compares

The nearestRivals list is empty in the the benchmark database, so no direct comparative analysis can be made against specific models. The percentileVsAllGpus of 50 indicates a median position in the database, but without rival names or scores, the comparison must be framed by the card’s own specifications. Against hypothetical consumer Ampere cards, the PG506-217 sacrifices shading units and RT cores for memory bandwidth and tensor throughput. Against server counterparts, its 165 W TDP is low, suggesting it is a value-oriented compute accelerator.

The lack of display outputs and graphics APIs means it cannot compete in gaming benchmarks, where rasterization performance dominates. In compute benchmarks, the 933.1 GB/s bandwidth and 24 GB capacity are likely strengths, but the 10.32 TFLOPS FP32 is moderate. The 50th percentile ranking suggests it is neither a top performer nor a weak one, it occupies a niche for memory-intensive, tensor-accelerated tasks. Without rival data, the safest conclusion is that this card fills a specific role: high-bandwidth compute at low power, but not a general-purpose graphics solution.