NVIDIA Tesla V100S PCIe 32 GB

The NVIDIA Tesla V100S PCIe 32 GB is a data-center compute accelerator built on the Volta architecture, fabricated on TSMC's 12 nm process with 21,100 million transistors across an 815 mm² die. It pairs 5,120 shading units with 640 tensor cores and 32 GB of HBM2 memory on a 4096-bit bus, delivering 1.13 TB/s of bandwidth. The card targets high-throughput FP32 and FP16 workloads, with a base clock of 1245 MHz and a boost clock of 1597 MHz. It is now end-of-life, having launched in late 2019, and the database places it at the 50th percentile of all tracked GPUs, indicating median performance within that historical context.

Benchmark Performance

The database does not list any benchmark scores for the Tesla V100S PCIe 32 GB, so performance analysis relies on its theoretical compute metrics and its percentile ranking. The FP32 throughput is 16.35 TFLOPS, while FP16 reaches 32.71 TFLOPS via a 2:1 ratio. These figures position the card as a capable compute device, though the 50th percentile ranking suggests that, among all GPUs in the database, it sits exactly in the middle of the performance distribution. This is notable for a card that was designed for server workloads rather than consumer gaming, and the median placement reflects the fact that many modern consumer GPUs now exceed its raw FP32 output.

The pixel rate of 204.4 GPixel/s and texture rate of 511.0 GTexel/s are derived from the 128 ROPs and 320 TMUs, respectively. These rates are competitive for a 2019 accelerator, but they do not directly translate to gaming performance since the card has no display outputs. The FP16 capability is double the FP32 rate, which is typical for Volta and advantageous for mixed-precision training. The tensor cores, of which there are 640, provide dedicated matrix math acceleration for AI inference and training, but the database does not provide a separate tensor performance score.

Given the absence of benchmark entries, the percentile figure is the only comparative metric. The 50th percentile implies that half of the GPUs in the database are faster and half are slower, based on aggregated scores. For a compute-oriented card with a high memory capacity, this median standing is somewhat surprising, but it likely reflects the card's age and the rapid advancement of both consumer and professional GPUs since its release. The lack of ray tracing cores further differentiates it from modern architectures.

Ray Tracing and Feature Set

The Tesla V100S PCIe 32 GB has no dedicated ray tracing cores; the field for RT cores is null. This is a deliberate design choice for a compute card, as ray tracing hardware is primarily relevant for real-time graphics, which this card does not handle due to the absence of display outputs. Instead, the card's feature set centers on tensor cores and general compute. The 640 tensor cores enable accelerated deep learning operations, and the card supports the Volta architecture's Tensor Core instructions for mixed-precision workloads.

In terms of API support, the card lists DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. These are graphics APIs, but without display outputs, they are largely irrelevant for end-user rendering. The Vulkan 1.4 support is notable as a recent API version, but again, it would only be used in headless compute contexts. The card also lacks any display outputs, so it cannot be used for video output or direct rendering. Its bus interface is PCIe 3.0 x16, which is standard for the era.

The power delivery requires two 8-pin connectors and a 600 W suggested PSU, with a TDP of 250 W. The dual-slot design accommodates the HBM2 stacks and cooling solution. There is no mention of hardware ray tracing or DLSS-like features, as those are Turing-era innovations. The card's feature set is squarely aimed at high-performance computing, scientific simulation, and AI training, where tensor cores and memory bandwidth matter more than graphics features.

Memory Subsystem

The memory subsystem is one of the strongest aspects of the Tesla V100S PCIe 32 GB. It features 32 GB of HBM2 memory on a 4096-bit bus, yielding a bandwidth of 1.13 TB/s. This is a massive amount of memory and bandwidth, especially for its time. The 32 GB capacity allows large models and datasets to reside in GPU memory without spilling to system RAM, which is critical for deep learning training and inference on large neural networks. The 4096-bit bus is among the widest ever implemented, and the 1.13 TB/s bandwidth enables rapid data movement for compute kernels.

The memory clock is listed as 1107 MHz, with an effective data rate of 2.2 Gbps. This is relatively modest compared to later GDDR6X implementations, but the sheer bus width compensates, resulting in the high aggregate bandwidth. For high-resolution scientific simulations or AI workloads that process multi-gigabyte matrices, this memory subsystem reduces bottlenecks. The 32 GB capacity also supports larger batch sizes in training, which can improve throughput.

The card's memory type, HBM2, is known for its energy efficiency and compact footprint, though it adds manufacturing complexity. The 1.13 TB/s figure is a raw specification, and real-world effective bandwidth may vary depending on access patterns. Still, for a 2019 card, this memory configuration was top-tier, and it remains competitive even by today's standards. The combination of 32 GB and 1.13 TB/s makes the card suitable for workloads that require both high capacity and high throughput, such as computational fluid dynamics, genomic analysis, and large-scale matrix operations.

How It Compares

The nearestRivals field is empty, meaning the database does not provide any direct competitor comparisons for this card. Consequently, a rival-by-rival breakdown is not possible. The only comparative data point is the 50th percentile ranking across all GPUs. This suggests that, in aggregate, the Tesla V100S PCIe 32 GB performs at the median of the database's entire GPU population. That median status is interesting because the card is a professional accelerator, not a gaming GPU, yet it still lands in the middle when all GPUs are considered.

Without rival entries, the analysis must rely on the card's own specification sheet. Its predecessor is listed as Tesla Pascal, and its successor as Tesla Turing. These are generational markers, not performance comparisons. The card bridges the Pascal and Turing eras, incorporating Volta's tensor cores while lacking the ray tracing hardware that Turing introduced. The empty rival list may indicate that the database does not have enough comparable entries for this specific SKU, or that its unique positioning (32 GB HBM2, no display outputs) does not align with typical consumer or workstation cards.

The 50th percentile is a useful anchor. It implies that the card is neither a top performer nor a low-end part in the historical context of the database. For compute workloads, the card's strengths are its memory capacity and bandwidth, which are not fully captured by generic benchmark scores. The lack of ray tracing cores and display outputs further limits its appeal for gaming or professional visualization, but those are not the intended use cases.

Who Should Consider It

The Tesla V100S PCIe 32 GB is designed for compute-intensive applications that require large memory and high bandwidth. Its 32 GB HBM2 and 1.13 TB/s bandwidth make it well-suited for deep learning training, where model sizes often exceed 16 GB, and for inference tasks that need to hold multiple models or large batches. The 640 tensor cores accelerate matrix operations, and the FP16 throughput of 32.71 TFLOPS is double the FP32 rate, which is beneficial for mixed-precision training. Researchers and data scientists working on natural language processing, computer vision, or scientific simulations could benefit from this card's capacity and throughput.

The card has no display outputs, so it is not for desktop use or gaming. It is a headless accelerator meant for servers and workstations that handle compute remotely. Its dual-slot form factor and 250 W TDP require a standard server chassis with adequate power. The 600 W suggested PSU is a guideline for a system with this card, but actual requirements depend on other components.

Given its end-of-life status, the card is now a legacy product. Buyers looking for current support or the latest features might consider newer accelerators, but the database does not provide comparative data for those. The 50th percentile ranking suggests that its performance is not exceptional by today's standards, but for specific workloads that leverage its memory size and bandwidth, it can still be a viable option, especially in secondary markets. The lack of ray tracing cores means it is unsuitable for real-time ray-traced rendering, but that is not a concern for compute-only deployments.

In summary, the Tesla V100S PCIe 32 GB is a specialized compute card that excels in memory-heavy AI and scientific workloads. Its 32 GB capacity and 1.13 TB/s bandwidth are its defining features, while its tensor cores provide acceleration for deep learning. The card sits at the median of the database's GPU population, reflecting its age and the rapid evolution of hardware. It is not a general-purpose GPU, and its target audience is organizations that need a high-memory, high-bandwidth compute accelerator for batch processing and model training.