NVIDIA Tesla PG503-216

How It Compares

The NVIDIA Tesla PG503-216 occupies a distinct position in the database, holding the 50th percentile among all GPUs. This places it squarely in the middle of the pack, neither a top-tier performer nor a low-end part. Its benchmark score of zero, however, indicates that no standardized benchmark results have been recorded for this specific card, making direct quantitative comparisons difficult. The data sheet shows no nearest rival entries, so positional analysis must rely on its architectural and specification profile rather than head-to-head scores.

Against the broader Tesla lineage, this card sits between the Tesla Pascal generation and the Tesla Turing successor. Its Volta architecture, built on a 12 nm TSMC process, represents a significant departure from the Pascal design that preceded it. The 21,100 million transistors packed into an 815 mm² die yield a transistor density of 25.9 million per square millimeter — a figure that underscores the complexity of this compute-oriented part. The sheer die size, one of the largest in the database, suggests a chip designed for heavy parallel workloads rather than consumer gaming.

The absence of any nearestRivals data means the card cannot be positioned against specific competing models with numeric deltas. Instead, its 50th percentile standing implies that half of all GPUs in the database outperform it and half underperform it, a balanced but unremarkable placement. This is consistent with a professional compute card that prioritizes memory capacity and tensor throughput over raw rasterization speed. The Tesla PG503-216 is end-of-life, having been released in late November 2019, and it has since been succeeded by the Tesla Turing generation.

Power and Cooling

The Tesla PG503-216 carries a thermal design power of 250 W, a figure that aligns with its dual-slot cooler design. The card requires a 600 W system power supply, as indicated by the suggested PSU specification. This recommendation accounts for the card's draw alongside other system components, providing a safe operating envelope for the entire platform. Notably, the card has no power connectors of its own — it draws power exclusively through its PCIe 3.0 x16 bus interface. This is a defining characteristic of Tesla accelerator cards, which are designed for server installations where power delivery is handled by the motherboard or backplane rather than direct PSU connections.

The dual-slot form factor means the card occupies two expansion slots in a chassis, a common configuration for high-TDP accelerators that require substantial heatsinks. The absence of display outputs further reinforces its server-oriented design; this is not a card intended for a desktop workstation with monitors attached. The cooling solution is not specified in terms of fan count or heatsink size, but the dual-slot footprint and 250 W TDP suggest a capable air cooler that can manage sustained compute loads. In a dense server environment, airflow becomes critical, and the dual-slot design provides adequate surface area for heat dissipation.

The memory subsystem, comprising 32 GB of HBM2 across a 4096-bit bus, generates additional thermal load beyond the core. The 1.13 TB/s memory bandwidth is substantial, and HBM2 stacks typically run cooler than GDDR6 due to their lower voltage and closer proximity to the processor. Still, the total thermal envelope remains within the 250 W TDP figure, meaning the core and memory together must stay within that power budget. For system integrators, the 600 W PSU recommendation provides headroom for a typical server configuration with a single CPU and several drives, though multiple accelerators would require a larger supply.

Benchmark Performance

With no recorded benchmark scores and no nearest rivals, the Tesla PG503-216's performance must be inferred from its raw specification sheet. The FP32 throughput is 14.10 TFLOPS, a figure that reflects the card's compute-oriented design. For comparison, the FP16 rate doubles to 28.20 TFLOPS via a 2:1 ratio, meaning the card can process half-precision math at twice the rate of single-precision. This is a hallmark of Volta architecture, which introduced dedicated tensor cores for AI workloads. The 640 tensor cores on this chip are designed to accelerate matrix operations commonly found in deep learning inference and training.

The pixel rate is 195.8 GPixel/s, derived from 128 ROPs operating at the boost clock. The texture rate of 440.6 GTexel/s comes from 288 TMUs. These figures suggest that while the card is not optimized for traditional gaming workloads — its 4,608 shading units are fewer than some consumer flagships — it still possesses enough rasterization capability for basic graphics tasks. However, the absence of RT cores means hardware-accelerated ray tracing is not available on this card, a notable omission for any graphics workload that relies on that feature.

The memory bandwidth of 1.13 TB/s is a standout specification, more than double what most consumer cards of that era offered. This bandwidth is critical for data-intensive workloads such as scientific simulations and large language model inference, where the GPU must rapidly feed data to the compute units. The 32 GB capacity further supports large datasets that would exceed the memory of typical gaming cards. In practical terms, the card can hold substantial portions of a neural network or a large simulation grid in on-chip memory, reducing the need for frequent host-to-device transfers.

Clock speeds are modest by gaming standards — 1312 MHz base and 1530 MHz boost — but they are consistent with a card that prioritizes sustained throughput over peak burst performance. The 12 nm process node, while not cutting-edge even at launch, allowed for high transistor counts without excessive power draw. The result is a balanced compute card that delivers predictable performance across FP32 and FP16 workloads, with the tensor cores providing an additional acceleration path for AI-specific operations.

FAQ

Q: Does the Tesla PG503-216 support hardware ray tracing?

A: No. The card has no RT cores listed in its specifications, and its architecture predates the RTX generation that introduced dedicated ray tracing hardware. It relies on traditional rasterization for any graphics output, though its primary use case is compute, not gaming.

Q: What is the memory bandwidth and capacity of this card?

A: The card features 32 GB of HBM2 memory on a 4096-bit bus, delivering 1.13 TB/s of bandwidth. The memory clock is 1106 MHz, which translates to 2.2 Gbps effective.

Q: How many tensor cores does the card have, and what are they used for?

A: The Tesla PG503-216 includes 640 tensor cores. These are dedicated processing units for matrix multiplication operations, commonly used in deep learning training and inference, as well as scientific computing tasks that rely on linear algebra.

Q: What power supply is recommended for this card?

A: The suggested PSU rating is 600 W. The card itself has a TDP of 250 W and draws power through its PCIe 3.0 x16 slot without any additional power connectors.

Q: What API support does the card offer?

A: The card supports DirectX 12 (feature level 12_1), OpenGL 4.6, and Vulkan 1.4. This provides compatibility with modern graphics APIs, though the card's lack of display outputs makes it unsuitable for direct rendering to a monitor.

Q: Is this card still in production?

A: No. The Tesla PG503-216 is marked as end-of-life in the database. Its production status is terminated, and it has been succeeded by the Tesla Turing generation.

Ray Tracing and Feature Set

The Tesla PG503-216 does not include any RT cores, meaning hardware-accelerated ray tracing is entirely absent from this card. This is a significant limitation for any workload that requires real-time ray-traced effects, such as photorealistic rendering or advanced game engines. The card instead focuses on compute throughput, with its 640 tensor cores providing acceleration for AI and deep learning tasks. These tensor cores operate on half-precision data, enabling the 28.20 TFLOPS FP16 throughput that is double the FP32 rate.

The API support includes DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, which ensures compatibility with modern graphics software. However, the card has no display outputs, so it cannot drive a monitor directly. This reinforces its role as an accelerator for servers and workstations where rendering happens remotely or where the GPU is used solely for computation. The Vulkan 1.4 support is particularly relevant for compute workloads, as Vulkan's compute shaders can leverage the card's raw processing power without the overhead of graphics pipelines.

The feature set is rounded out by the card's memory architecture. The 32 GB of HBM2 with 1.13 TB/s bandwidth is a defining characteristic, enabling the card to handle datasets that would cause smaller-memory GPUs to thrash. The 4096-bit bus width is exceptionally wide, allowing for high data throughput in parallel workloads. For professionals working with large models or simulations, this memory configuration provides a practical advantage over cards with comparable FP32 performance but smaller memory pools.

Who Should Consider It

The Tesla PG503-216 is designed for compute professionals, not gamers. Its lack of display outputs and RT cores, combined with its 32 GB memory capacity and tensor core acceleration, makes it suitable for deep learning researchers, scientific computing teams, and data center operators who need to process large datasets with high precision. The 14.10 TFLOPS FP32 performance and 28.20 TFLOPS FP16 performance provide a solid foundation for training neural networks or running complex simulations, while the 1.13 TB/s bandwidth ensures that data movement does not become a bottleneck.

For resolution-specific considerations, the card's 195.8 GPixel/s pixel rate and 440.6 GTexel/s texture rate are adequate for high-resolution rendering in software that supports compute-based graphics, but the absence of RT cores and display outputs means it is not a practical choice for direct gaming or workstation visualization. The 250 W TDP and dual-slot design fit into standard server chassis, and the 600 W PSU recommendation aligns with typical single-GPU server configurations.

The card occupies a niche that has since been filled by newer Tesla Turing parts, which likely offer improved ray tracing and tensor performance. However, for environments where existing software is optimized for Volta architecture, the PG503-216 remains a capable option, particularly for tasks that require large memory capacities. The 32 GB HBM2 pool is a distinct advantage over many modern cards that cap at 16 GB or 24 GB, and the wide 4096-bit bus ensures that memory-intensive workloads see minimal latency. In summary, this card is best suited for compute-heavy deployments where FP16 throughput and memory bandwidth take precedence over graphics features and where the absence of RT cores is not a limiting factor.