NVIDIA Tesla V100 SXM2 32 GB vs NVIDIA TITAN X Pascal Comparison

The Verdict

The data places the NVIDIA Tesla V100 SXM2 32 GB and the NVIDIA TITAN X Pascal at opposite ends of the compute spectrum, despite both being flagship-class NVIDIA parts from the same era. The V100 SXM2 32 GB is the clear performance leader in every recorded benchmark, averaging 137,731 across its two tests against the TITAN X Pascal’s 72,098. That is a 91% advantage in average benchmark score, a margin that reflects fundamentally different design priorities. The V100 sits in the 96th percentile of all GPUs, while the TITAN X Pascal sits in the 91st percentile. The verdict is straightforward: the Tesla V100 SXM2 32 GB is for workloads that demand maximum compute throughput and memory bandwidth, while the TITAN X Pascal is a capable consumer-class card that trails in raw compute but offers display outputs and a conventional slot design.

For users prioritizing compute-intensive tasks such as scientific simulation, machine learning training, or data-center acceleration, the V100 SXM2 32 GB is the only rational choice from this pair. Its Geekbench OpenCL score of 131,696 and Vulkan score of 143,765 dwarf the TITAN X Pascal’s 66,696 and 77,499, respectively. The delta is enormous: 97.5% ahead in OpenCL and 85.5% ahead in Vulkan. For anyone needing a GPU for gaming, desktop rendering, or any workload that requires video outputs, the TITAN X Pascal is the only one of the two that can drive a display, as the V100 SXM2 has no display outputs. The TITAN X Pascal also has a launch MSRP of 1,199 USD, which the database records, but the V100’s price is not listed, so no direct cost comparison is possible from this data.

Architecture Differences

The two GPUs are built on different architectures, processes, and memory systems, which explains the performance gulf. The Tesla V100 SXM2 32 GB uses the GV100 chip on the Volta architecture, fabricated on a 12 nm process at TSMC. It packs 21,100 million transistors on an 815 mm² die, yielding a transistor density of 25.9 million per mm². The TITAN X Pascal uses the GP102 chip on the Pascal architecture, also from TSMC but on a 16 nm process. It contains 11,800 million transistors on a 471 mm² die, with a density of 25.1 million per mm². The V100’s die is nearly twice the physical size and carries almost double the transistor count.

The compute resources differ sharply. The V100 has 5,120 shading units, 320 texture mapping units, 128 ROPs, and 640 tensor cores. The TITAN X Pascal has 3,584 shading units, 224 TMUs, 96 ROPs, and no tensor cores. The V100’s FP32 throughput is 15.67 TFLOPS, while the TITAN X Pascal reaches 10.97 TFLOPS. More striking is the FP16 difference: the V100 delivers 31.33 TFLOPS with a 2:1 ratio, whereas the TITAN X Pascal manages only 171.5 GFLOPS at a 1:64 ratio. This makes the V100 over 180 times faster in FP16, a critical feature for AI and deep learning workloads that rely on reduced precision.

Memory is another major divergence. The V100 uses 32 GB of HBM2 on a 4096-bit bus, providing 898.0 GB/s of bandwidth. The TITAN X Pascal has 12 GB of GDDR5X on a 384-bit bus, delivering 480.4 GB/s. The V100 has nearly double the bandwidth and nearly triple the capacity. Clock speeds are similar in boost (1530 MHz for the V100 versus 1531 MHz for the TITAN X Pascal), but the V100’s base clock is lower at 1290 MHz versus 1417 MHz. The V100’s memory clock is 877 MHz (1754 Mbps effective), while the TITAN X Pascal runs at 1251 MHz (10 Gbps effective). The TITAN X Pascal’s higher memory clock is irrelevant given its narrower bus and slower memory type.

Physical design also separates them. The V100 is an SXM module with no power connectors and no display outputs, requiring a host system to supply power through the socket. The TITAN X Pascal is a dual-slot card, 267 mm long, 112 mm tall, and 40 mm wide, with 1x 6-pin and 1x 8-pin power connectors, and outputs including 1x DVI, 1x HDMI 2.0, and 3x DisplayPort 1.4a. Both share the same 250 W TDP and 600 W suggested PSU. Both support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, and both run on PCIe 3.0 x16.

Head-to-Head Benchmarks

The recorded head-to-head data shows a clean sweep for the Tesla V100 SXM2 32 GB across both benchmark tests. In Geekbench OpenCL, the V100 scores 131,696 against the TITAN X Pascal’s 66,696, a delta of 97.5%. This means the V100 is nearly twice as fast in OpenCL compute. The margin is not a small edge; it is a doubling of performance. In Geekbench Vulkan, the V100 scores 143,765 against 77,499, a delta of 85.5%. Again, the V100 is far ahead, though the Vulkan gap is slightly smaller than the OpenCL gap. The V100’s Vulkan score is higher than its OpenCL score, while the TITAN X Pascal’s Vulkan score is also higher than its OpenCL score, but the relative advantage remains consistent.

These results align with the architectural differences. The V100’s 640 tensor cores and 2:1 FP16 throughput give it a massive edge in workloads that leverage these units, and the Geekbench suite clearly exercises compute paths where the V100 excels. The TITAN X Pascal, with its 1:64 FP16 ratio and no tensor cores, cannot compete in these tests. The V100 wins both tests, recording 2 wins to 0 for the TITAN X Pascal in the head-to-head table.

The average benchmark scores reinforce the same story. The V100’s average is 137,731, placing it among the top GPUs in the 96th percentile. Its nearest rivals include the AMD Instinct MI100 at 139,035 (the V100 trails by 0.9%), the NVIDIA Tesla V100 PCIe 16 GB at 138,063 (the V100 trails by 0.2%), the AMD Radeon PRO V620 at 136,472 (the V100 leads by 0.9%), and the AMD Radeon Pro W6800X Duo at 135,774 (the V100 leads by 1.4%). The TITAN X Pascal’s average is 72,098, in the 91st percentile, with rivals including the AMD Radeon Vega Frontier Edition at 73,370 (the TITAN trails by 1.7%), the AMD Radeon Pro Vega 64 at 72,379 (the TITAN trails by 0.4%), the AMD Radeon RX 6650M at 71,768 (the TITAN leads by 0.5%), and the AMD Radeon RX 6600 LE at 70,829 (the TITAN leads by 1.8%). The V100’s nearest rivals are all professional or data-center cards, while the TITAN X Pascal’s nearest rivals include consumer and mobile parts, reflecting its lower position in the performance hierarchy.

FAQ

Q: Which GPU has more memory bandwidth?

A: The NVIDIA Tesla V100 SXM2 32 GB has 898.0 GB/s of bandwidth, more than double the NVIDIA TITAN X Pascal’s 480.4 GB/s.

Q: Does the TITAN X Pascal support FP16 compute at a useful rate?

A: No. The TITAN X Pascal provides 171.5 GFLOPS of FP16 at a 1:64 ratio, which is negligible compared to the V100’s 31.33 TFLOPS at a 2:1 ratio.

Q: Can the Tesla V100 SXM2 32 GB be used with a standard desktop monitor?

A: No. The V100 has no display outputs. The TITAN X Pascal includes 1x DVI, 1x HDMI 2.0, and 3x DisplayPort 1.4a.

Q: Which GPU is closer to its nearest rival in average score?

A: The TITAN X Pascal is closer to its nearest rival, the AMD Radeon Pro Vega 64, with a delta of only 0.4%. The V100’s closest rival is the NVIDIA Tesla V100 PCIe 16 GB, with a delta of 0.2%.

Q: What is the transistor density difference between the two?

A: The V100 has a density of 25.9 million transistors per mm², while the TITAN X Pascal has 25.1 million per mm². Despite the V100’s larger die (815 mm² versus 471 mm²), the density is similar.

Q: Do both GPUs have the same power draw?

A: Yes, both have a TDP of 250 W and a suggested PSU of 600 W. The V100 uses an SXM module with no power connectors, while the TITAN X Pascal uses 1x 6-pin and 1x 8-pin connectors.

Where Each One Wins

The Tesla V100 SXM2 32 GB wins every recorded benchmark, and the data points to specific strengths. In OpenCL compute, it is 97.5% ahead of the TITAN X Pascal, which makes it the superior choice for any workload that stresses general-purpose compute, such as scientific computing, physics simulation, or data processing. Its 32 GB of HBM2 memory at 898.0 GB/s means it can hold larger datasets and move data faster, benefiting workloads like large model inference or high-resolution volume rendering. The 640 tensor cores and 31.33 TFLOPS FP16 throughput give it a decisive edge in machine learning training and inference, where reduced precision is the norm. The V100 also sits in the 96th percentile of all GPUs, indicating it belongs to the top tier of compute hardware. Its nearest rivals are all professional accelerators, confirming its position in the data-center and workstation segment.

The TITAN X Pascal wins only in practical versatility, not raw performance. It has display outputs, which the V100 lacks, so it can serve as a desktop GPU for gaming, content creation, or any task that requires a monitor. Its dual-slot design and standard power connectors make it easier to install in a conventional PC, and its 12 GB GDDR5X memory, while smaller and slower, is still substantial for its era. Its FP32 throughput of 10.97 TFLOPS is no small number, but it is only 70% of the V100’s 15.67 TFLOPS. The TITAN X Pascal’s Vulkan score of 77,499 and OpenCL score of 66,696 place it in the 91st percentile, which is respectable but clearly below the V100’s level. Its nearest rivals include consumer and mobile GPUs, showing that it competes in the upper-midrange of the broader GPU market.

In a use-case split, the V100 SXM2 32 GB is the pick for headless compute nodes, AI training clusters, and any environment where the lack of display outputs is irrelevant. The TITAN X Pascal is the pick for a single-user workstation that needs both compute and display capability, though it will deliver roughly half the compute performance in the recorded tests. The data does not support any scenario where the TITAN X Pascal outperforms the V100 in raw throughput, so the choice comes down to form factor and connectivity, not performance. For anyone who can accommodate an SXM module and does not need video output, the V100 is unambiguously the stronger GPU. For everyone else, the TITAN X Pascal remains a functional alternative, but the benchmark gap is so large that it should only be chosen when the V100’s module format is a hard constraint.