NVIDIA Tesla V100S PCIe 32 GB vs NVIDIA TITAN X Pascal Comparison

Head-to-Head Benchmarks

The recorded database contains a single shared benchmark between these two accelerators, and the result is emphatic. In Geekbench OpenCL, the NVIDIA Tesla V100S PCIe 32 GB scores 194,415 points, while the NVIDIA TITAN X Pascal scores 66,696 points. That is a delta of 191.5%, meaning the Tesla V100S delivers nearly triple the compute throughput of the TITAN X in this workload. The win count is 1 for the Tesla V100S and 0 for the TITAN X Pascal; there is no benchmark in the database where the TITAN X comes out ahead. This is not a marginal victory, it is a generational gap made visible in a single number.

Context matters here. The Tesla V100S sits at the 98th percentile of all GPUs in the database, while the TITAN X Pascal sits at the 91st percentile. Both are high performers, but the percentile gap reflects how far apart they are in absolute terms. The Tesla V100S is 0.8% behind the NVIDIA RTX 6000D (which averages 195,964), 3.9% ahead of the NVIDIA A100 SXM4 40 GB (187,147), 5.3% ahead of the NVIDIA RTX 5000 Ada Generation (184,664), and 5.8% ahead of the NVIDIA A100 SXM4 80 GB (183,725). The TITAN X Pascal, by contrast, is 0.4% behind the AMD Radeon Pro Vega 64 (72,379), 0.5% ahead of the AMD Radeon RX 6650M (71,768), 1.8% ahead of the AMD Radeon RX 6600 LE (70,829), and 1.7% behind the AMD Radeon Vega Frontier Edition (73,370). In other words, the TITAN X trades blows with modern mid-range and workstation parts, while the Tesla V100S is operating in a category that includes data-center accelerators from a newer generation.

The OpenCL result alone does not tell the full story of every possible workload, but it is the only head-to-head measurement available. The data implies that the Tesla V100S is not just faster, it is faster by a margin large enough to shift which class of GPU a buyer should consider. A 191.5% advantage in a compute-oriented API like OpenCL is the kind of result that separates a consumer-derived product from a dedicated compute card.

FAQ

Q: Which GPU wins the only head-to-head benchmark in the database?

A: The NVIDIA Tesla V100S PCIe 32 GB wins the Geekbench OpenCL test with a score of 194,415 against 66,696 for the NVIDIA TITAN X Pascal, a delta of 191.5%.

Q: How does the Tesla V100S compare to its nearest rivals?

A: The Tesla V100S is 0.8% behind the NVIDIA RTX 6000D, and it is ahead of the NVIDIA A100 SXM4 40 GB by 3.9%, the NVIDIA RTX 5000 Ada Generation by 5.3%, and the NVIDIA A100 SXM4 80 GB by 5.8%.

Q: How does the TITAN X Pascal compare to its nearest rivals?

A: The TITAN X Pascal is 0.4% behind the AMD Radeon Pro Vega 64 and 1.7% behind the AMD Radeon Vega Frontier Edition, while it is 0.5% ahead of the AMD Radeon RX 6650M and 1.8% ahead of the AMD Radeon RX 6600 LE.

Q: What is the average benchmark score for each GPU?

A: The Tesla V100S has an average benchmark score of 194,415, while the TITAN X Pascal has an average benchmark score of 72,098 based on its OpenCL and Vulkan results.

Q: Does the TITAN X Pascal have any benchmark win over the Tesla V100S?

A: No, the database records zero wins for the TITAN X Pascal and one win for the Tesla V100S in their head-to-head comparison.

Q: What is the percentile ranking of each GPU?

A: The Tesla V100S ranks at the 98th percentile of all GPUs, while the TITAN X Pascal ranks at the 91st percentile.

Architecture Differences

The two GPUs come from different architecture generations and are built on different process nodes. The Tesla V100S uses the GV100 chip on the Volta architecture, manufactured by TSMC on a 12 nm process. The TITAN X Pascal uses the GP102 chip on the Pascal architecture, also from TSMC but on a 16 nm process. The transistor counts reflect the scale difference: the GV100 packs 21,100 million transistors on a die size of 815 mm², giving a transistor density of 25.9 million per mm². The GP102 contains 11,800 million transistors on a 471 mm² die, with a density of 25.1 million per mm². The Volta chip is physically larger and denser, which explains part of its compute advantage.

The most significant architectural divergence is the presence of tensor cores. The Tesla V100S includes 640 tensor cores, while the TITAN X Pascal has none. Tensor cores are dedicated hardware for matrix math, and their absence in the Pascal part means the TITAN X cannot accelerate certain AI and deep-learning workloads in the same way. The shading unit count also differs: the Tesla V100S has 5,120 shading units, 320 texture mapping units, and 128 ROPs, while the TITAN X Pascal has 3,584 shading units, 224 TMUs, and 96 ROPs. The Tesla V100S therefore has roughly 43% more shading units, 43% more TMUs, and 33% more ROPs, all of which contribute to its higher pixel rate of 204.4 GPixel/s versus 147.0 GPixel/s for the TITAN X.

FP16 throughput is another major split. The Tesla V100S delivers 32.71 TFLOPS of FP16 with a 2:1 ratio relative to FP32, while the TITAN X Pascal delivers 171.5 GFLOPS of FP16 with a 1:64 ratio. That is a difference of two orders of magnitude in half-precision performance, which is critical for machine-learning training and inference workloads that rely on reduced precision. FP32 performance also favors the Tesla V100S at 16.35 TFLOPS versus 10.97 TFLOPS for the TITAN X, a 49% advantage. Texture rate follows the same pattern: 511.0 GTexel/s for the Tesla V100S against 342.9 GTexel/s for the TITAN X. Both GPUs support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, and neither has ray tracing cores.

Specification Differences

The memory subsystems are fundamentally different. The Tesla V100S has 32 GB of HBM2 on a 4096-bit bus, delivering 1.13 TB/s of bandwidth. The TITAN X Pascal has 12 GB of GDDR5X on a 384-bit bus, delivering 480.4 GB/s. That is a 2.35x bandwidth advantage for the Tesla V100S and nearly three times the memory capacity. The memory clock is reported as 1107 MHz with 2.2 Gbps effective for the Tesla V100S, versus 1251 MHz with 10 Gbps effective for the TITAN X. The core clocks tell a different story: the TITAN X Pascal has a higher base clock at 1417 MHz versus 1245 MHz, and a higher boost clock at 1531 MHz versus 1597 MHz? No, the Tesla V100S boost clock is 1597 MHz, which is actually higher than the TITAN X's 1531 MHz. The TITAN X does start at a higher base frequency, but the Tesla V100S has a higher boost ceiling. Both have a 250 W TDP and a suggested PSU of 600 W, and both are dual-slot cards with PCIe 3.0 x16 interfaces.

The power connectors differ: the Tesla V100S requires 2x 8-pin connectors, while the TITAN X Pascal uses 1x 6-pin + 1x 8-pin. The display outputs are also different. The Tesla V100S has no display outputs, meaning it is designed for compute-only environments without any video output capability. The TITAN X Pascal includes 1x DVI, 1x HDMI 2.0, and 3x DisplayPort 1.4a outputs, making it usable as a display-driving workstation card. The TITAN X Pascal has recorded physical dimensions of 267 mm length, 112 mm height, 40 mm width, while the Tesla V100S does not list dimensions in the database. The TITAN X Pascal has a launch MSRP of 1,199 USD, which may be stated once for reference; the Tesla V100S has no launch MSRP recorded.

Release dates place them in different eras: the Tesla V100S was released on 2019-11-25, while the TITAN X Pascal was released on 2016-08-01. The Tesla V100S lists Tesla Pascal as its predecessor and Tesla Turing as its successor, and the TITAN X lists GeForce 900 as its predecessor and GeForce 20 as its successor. Both are end-of-life products. The generation field describes the Tesla as Tesla Volta and the TITAN X as GeForce 10.

Where Each One Wins

The Tesla V100S wins in every measurable compute scenario recorded in the database, but the nature of those wins points to a clear use-case split. For OpenCL-heavy applications such as scientific simulation, scientific visualization, data analytics, and general-purpose compute, the Tesla V100S is the only choice between these two based on the 191.5% score advantage. The 32 GB HBM2 memory and 1.13 TB/s bandwidth make it suited for large datasets that would not fit in the TITAN X's 12 GB GDDR5X frame buffer. The 640 tensor cores and 32.71 TFLOPS of FP16 throughput position it for machine-learning training and inference, workloads that the TITAN X Pascal cannot meaningfully accelerate due to its lack of tensor cores and its tiny 171.5 GFLOPS FP16 rate. The Tesla V100S also wins on pixel rate (204.4 GPixel/s vs 147.0 GPixel/s) and texture rate (511.0 GTexel/s vs 342.9 GTexel/s), suggesting advantages in geometry-heavy rendering pipelines, though its lack of display outputs means it cannot drive a monitor directly.

The TITAN X Pascal wins in scenarios where display output matters. It has 1x DVI, 1x HDMI 2.0, and 3x DisplayPort 1.4a, so it can serve as a conventional workstation GPU for desktop workloads that need both compute and visual output. Its higher base clock of 1417 MHz gives it a slight edge in lightly threaded workloads that do not scale with clock speed, though its lower boost ceiling of 1531 MHz limits sustained performance. The 12 GB memory is smaller but still substantial for moderate datasets, and the GDDR5X memory at 10 Gbps effective is adequate for graphics-oriented tasks. The TITAN X also has a smaller physical footprint with recorded dimensions, making mechanical installation easier in systems where space is constrained, and it requires only a 1x 6-pin + 1x 1x 8-pin power setup compared to the Tesla V100S's 2x 8-pin. Its 1,199 USD launch MSRP is a data point for historical reference, and its 91st percentile ranking shows it remains competitive with modern mid-range cards.

The Verdict

The data points to a clear conclusion: the NVIDIA Tesla V100S PCIe 32 GB is the superior compute accelerator, and the NVIDIA TITAN X Pascal is the better choice only for users who need display outputs or who find the TITAN X's lower power connector requirement and smaller physical size preferable. For any workload measured in the database, the Tesla V100S wins outright. The 191.5% OpenCL delta is not a close call; it is a decisive gap that dwarfs the differences between the Tesla V100S and its own nearest rivals, which range from 0.8% behind to 5.8% ahead. The TITAN X Pascal, meanwhile, sits within 1.8% of its nearest competitors, showing that it is a mid-pack performer in the modern landscape.

Buyers should pick the Tesla V100S if their priority is raw compute, large memory capacity, high bandwidth, or tensor-core-accelerated workloads. The 32 GB HBM2 frame buffer, 1.13 TB/s bandwidth, and 640 tensor cores make it a data-center-oriented part with no display capability but enormous processing headroom. Buyers should pick the TITAN X Pascal if they need a GPU that can both compute and drive displays, if they want to avoid the 2x 8-pin power requirement, or if they are working within the constraints of its 267 mm length and 112 mm height. Its 91st percentile ranking is respectable, but its 12 GB memory and lack of tensor cores limit its relevance for modern AI workloads. The verdict from the database is unambiguous: the Tesla V100S is the faster part by a wide margin, and the TITAN X Pascal is the more versatile consumer-adjacent option with display outputs and a lower power connector burden, but it cannot match the Tesla V100S in any recorded benchmark.