NVIDIA Tesla V100 PCIe 16 GB

# NVIDIA Tesla V100 PCIe 16 GB

The NVIDIA Tesla V100 PCIe 16 GB is a data-center oriented accelerator built on the Volta architecture, fabricated on TSMC's 12 nm process. It packs 21,100 million transistors onto an 815 mm² die, yielding a transistor density of 25.9 million transistors per mm². The card operates with a base clock of 1245 MHz and a boost clock of 1380 MHz, delivering 14.13 TFLOPS of FP32 compute and 28.26 TFLOPS of FP16 compute (at a 2:1 ratio). With a 300 W TDP, dual-slot cooler, and dual 8-pin power connectors, it is positioned for server racks rather than consumer desktops. Notably, its percentile rank against all GPUs is 50, indicating median performance in the broader database, though its benchmark score is listed as zero, meaning no direct performance samples are available for this entry.

Benchmark Performance

The data for the Tesla V100 PCIe 16 GB shows no direct benchmark scores — the `benchmarks` array is empty and the average benchmark score is zero. This is typical for compute-oriented accelerators that are rarely subjected to gaming or synthetic graphics workloads. However, the raw compute specifications provide a clear picture of its capability. The FP32 throughput of 14.13 TFLOPS places it in a range that would be competitive with high-end consumer GPUs of its generation, though the percentile rank of 50 suggests that in the full database of all GPUs — which includes both gaming and professional parts — it sits exactly at the median. This is an unusual position for a card with such a large die and high transistor count, but it underscores that the V100 is optimized for specific workloads, not general-purpose graphics.

The FP16 performance of 28.26 TFLOPS is exactly double the FP32 figure, reflecting the 2:1 ratio enabled by the Volta architecture's tensor cores. This ratio is critical for deep learning training and inference, where reduced precision is acceptable. The pixel rate of 176.6 GPixel/s and texture rate of 441.6 GTexel/s, derived from 128 ROPs and 320 TMUs respectively, indicate that the card is not starved for rasterization resources, but these figures pale next to its compute capabilities. In the absence of rival data — the `nearestRivals` array is empty — no direct percentage deltas can be cited. The analysis must therefore rely on the card's own specifications and its percentile placement to infer relative standing.

How It Compares

Given that the `nearestRivals` list is empty, there are no direct competitor comparisons available from the FACT PACK. The percentile rank of 50, however, offers a baseline: half of all GPUs in the database score higher, and half score lower. This is a surprisingly middling result for a card of this caliber, but it likely reflects the fact that the database includes many consumer gaming GPUs that excel in rasterization benchmarks, while the V100's strengths lie in compute tasks that are not captured by those metrics. The predecessor is listed as Tesla Pascal, and the successor as Tesla Turing, but no specific models or scores are provided for either.

Without rival names, scores, or deltaPct values, the comparison section can only note the absence of data. The card's position in the product stack is clear: it belongs to the Volta generation, sits between Pascal and Turing in the Tesla line, and targets HPC and AI workloads. Its 5120 shading units and 640 tensor cores are the key differentiators, but without competitor figures, relative performance cannot be quantified. The lack of display outputs reinforces that this is not a card for interactive use; it is a compute accelerator designed for servers and workstations where headless operation is standard.

Who Should Consider It

The Tesla V100 PCIe 16 GB is not suited for typical gaming or consumer desktop use — it has no display outputs, and its drivers and firmware are tuned for compute workloads. For users running deep learning frameworks, scientific simulations, or data processing pipelines that leverage FP16 or FP32 compute, the card's specifications are compelling on paper. The 16 GB of HBM2 memory with 897.0 GB/s of bandwidth is particularly relevant for large models or datasets that exceed the VRAM capacity of consumer cards. At 1080p or 1440p gaming, the card's FP32 performance of 14.13 TFLOPS would theoretically handle high settings, but the absence of display outputs makes this moot.

For resolution-specific recommendations grounded in the available data: at 4K, the high memory bandwidth of 897.0 GB/s would be advantageous for texture-heavy workloads, but again, without benchmark scores, any claim about playable frame rates is unsupported. The card's 4096-bit memory bus is exceptionally wide, which helps maintain throughput in memory-bound tasks. Users who need a headless accelerator for server rooms, with a 300 W TDP and 700 W suggested PSU, will find the V100's specifications align with that use case. Cloud service providers and research institutions are the primary audience, not individual consumers.

FAQ

Q: What is the FP32 performance of the Tesla V100 PCIe 16 GB?

A: The card delivers 14.13 TFLOPS of FP32 compute, based on a base clock of 1245 MHz and a boost clock of 1380 MHz across 5120 shading units.

Q: Does this card have tensor cores?

A: Yes, it includes 640 tensor cores, which enable FP16 compute at 28.26 TFLOPS, exactly double the FP32 rate (2:1 ratio).

Q: What type of memory does it use and how much bandwidth does it offer?

A: It uses 16 GB of HBM2 memory on a 4096-bit bus, providing 897.0 GB/s of memory bandwidth.

Q: Can this card output video to a display?

A: No, the card has no display outputs; it is designed for headless compute workloads in servers or workstations.

Q: What is the power requirement for this card?

A: It has a 300 W TDP, requires two 8-pin power connectors, and the suggested PSU is 700 W.

Q: What API support is available?

A: The card supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, though its primary use case is compute rather than graphics.

Ray Tracing and Feature Set

The Tesla V100 PCIe 16 GB does not include dedicated ray tracing cores — the `rtCores` field is null. This distinguishes it from later Turing-generation cards that introduced hardware RT acceleration. Instead, the card's feature set is centered on its 640 tensor cores, which are designed for matrix math used in AI and deep learning. The Volta architecture introduced these tensor cores, and their presence at this density (640 units) is the defining hardware feature. The card does not support real-time ray tracing via dedicated hardware, but it can theoretically handle compute-based ray tracing through general-purpose shaders, though without RT cores, performance would be limited.

The API support includes DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, which are relevant for compute and graphics workloads. The lack of display outputs means that any graphics API usage would be offscreen rendering or compute shaders. The FP16 capability at 2:1 ratio is a major feature for mixed-precision training, and the 12 nm process node from TSMC, with 21,100 million transistors, indicates a mature but not cutting-edge manufacturing process. The card is end-of-life, with a release date of 2017-06-20, and it is part of the Tesla Volta generation, with Tesla Pascal as predecessor and Tesla Turing as successor.

Memory Subsystem

The memory subsystem is one of the V100's strongest attributes. It pairs 16 GB of HBM2 memory with a 4096-bit bus, yielding a bandwidth of 897.0 GB/s. This is significantly higher than typical GDDR5 or GDDR6 configurations of the same era, and the wide bus allows for efficient data movement in memory-bound workloads. For high-resolution compute tasks — such as training large neural networks or processing 4K video frames — the bandwidth ensures that the 5120 shading units and 640 tensor cores are not starved for data. The memory clock runs at 876 MHz, with an effective data rate of 1752 Mbps.

The 16 GB capacity is substantial for 2017, allowing models or datasets that would exceed the 8 GB or 11 GB capacities of contemporary consumer cards. At 4K resolution, the bandwidth is more than sufficient for texture streaming, though the card's lack of display outputs makes this a moot point for gaming. For scientific computing, the memory subsystem's bandwidth is a bottleneck reliever: the 897.0 GB/s figure means that large matrix operations can be fed quickly to the compute units. The combination of 4096-bit bus and HBM2 type is a clear differentiator from cards using narrower GDDR6 buses, and it explains the 300 W TDP — high-bandwidth memory requires power. The 700 W suggested PSU accounts for the card's draw plus system overhead, and the dual 8-pin connectors supply the necessary current.