NVIDIA A100 PCIe 80 GB vs NVIDIA TITAN X Pascal Comparison

FAQ

Q: How much faster is the NVIDIA A100 PCIe 80 GB than the NVIDIA TITAN X Pascal in the recorded benchmark?

A: In the Geekbench OpenCL test, the A100 scores 207,124 versus the TITAN X Pascal’s 66,696. This gives the A100 a 210.5% advantage, meaning it is more than three times faster in this workload.

Q: Which GPU has the higher memory bandwidth?

A: The A100 PCIe 80 GB delivers 1.94 TB/s of bandwidth from 80 GB of HBM2e memory on a 5120-bit bus. The TITAN X Pascal provides 480.4 GB/s from 12 GB of GDDR5X on a 384-bit bus. The A100’s bandwidth is roughly four times higher.

Q: What are the transistor counts and process nodes for these two cards?

A: The A100 uses TSMC’s 7 nm process with 54,200 million transistors on an 826 mm² die. The TITAN X Pascal uses TSMC’s 16 nm process with 11,800 million transistors on a 471 mm² die. The A100 has a transistor density of 65.6M per mm², while the TITAN X has 25.1M per mm².

Q: Does the TITAN X Pascal support any APIs that the A100 lacks?

A: Yes. The TITAN X Pascal lists DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4 support. The A100’s API fields are null in the database, reflecting its server-oriented design with no display outputs.

Q: What are the FP32 and FP16 compute figures for each card?

A: The A100 achieves 19.49 TFLOPS FP32 and 77.97 TFLOPS FP16 (4:1 ratio). The TITAN X Pascal reaches 10.97 TFLOPS FP32 but only 171.5 GFLOPS FP16 (1:64 ratio). The A100 is 1.8 times faster in FP32 and dramatically faster in FP16.

Q: Which card has a higher percentile ranking among all GPUs?

A: The A100 sits at the 99th percentile, while the TITAN X Pascal ranks at the 91st percentile. The A100’s average benchmark score is 207,124, whereas the TITAN X’s average is 72,098.

The Verdict

The data is unambiguous: the NVIDIA A100 PCIe 80 GB is in a completely different performance class from the NVIDIA TITAN X Pascal. In the single head-to-head benchmark available, the A100 wins decisively with a 210.5% higher score. The A100 also holds the 99th percentile position versus the TITAN X’s 91st, confirming its placement among the fastest accelerators ever recorded.

For compute workloads, especially those leveraging FP16 or requiring massive memory capacity, the A100 is the clear choice. Its 80 GB HBM2e frame buffer and 1.94 TB/s bandwidth dwarf the TITAN X’s 12 GB GDDR5X and 480.4 GB/s. The A100 also brings tensor cores (432 of them) and a 7 nm architecture, both absent from the older Pascal design.

The TITAN X Pascal retains relevance only in scenarios where its API support matters, such as DirectX 12 or Vulkan workloads, since the A100 has no recorded API entries. Additionally, the TITAN X’s display outputs (1x DVI, 1x HDMI 2.0, 3x DisplayPort 1.4a) make it usable for graphics output, while the A100 has no outputs at all. For pure compute density, however, the A100 wins every measurable category.

Buyers needing maximum OpenCL throughput, high-bandwidth memory, or next-generation compute features should select the A100. Those requiring legacy API compatibility or a card with display connectivity for workstation use may consider the TITAN X, but they must accept roughly one-third of the A100’s raw performance.

Head-to-Head Benchmarks

The only direct comparison in the database is the Geekbench OpenCL test, and the result is lopsided. The A100 scores 207,124, the TITAN X scores 66,696, producing a delta of 210.5% in favor of the A100. This means the A100 achieves more than three times the OpenCL performance of the older card.

Context from the nearest rivals reinforces the gap. The A100’s closest competitor is the AMD Radeon PRO W7900D at 219,827, which is 5.8% ahead, and the NVIDIA PG506-232 at 225,124, which is 8% ahead. The A100 leads the NVIDIA RTX 6000D by 5.7% and the Tesla V100S PCIe 32 GB by 6.5%. These deltas are small compared to the 210% separation from the TITAN X, showing that the A100 competes in a far higher performance tier.

The TITAN X’s nearest rivals all sit within a narrow band. The AMD Radeon Pro Vega 64 scores 72,379 (0.4% behind), the AMD Radeon RX 6650M scores 71,768 (0.5% ahead), the AMD Radeon RX 6600 LE scores 70,829 (1.8% ahead), and the AMD Radeon Vega Frontier Edition scores 73,370 (1.7% behind). The TITAN X’s 72,098 average places it comfortably in that mid-range cluster, but the A100’s average of 207,124 is nearly three times higher.

The only other TITAN X benchmark, Geekbench Vulkan at 77,499, has no A100 counterpart in the database. Even that score, while higher than its OpenCL result, remains below the A100’s OpenCL figure by a factor of 2.7.

Specification Differences

The two cards differ in nearly every measurable specification. The A100 uses a GA100 chip built on 7 nm, while the TITAN X uses GP102 on 16 nm. Transistor counts are 54,200 million versus 11,800 million, and die sizes are 826 mm² versus 471 mm². The A100’s transistor density of 65.6M per mm² compares to 25.1M per mm² for the TITAN X.

Clock speeds favor the TITAN X in raw frequency: 1417 MHz base and 1531 MHz boost versus the A100’s 1065 MHz base and 1410 MHz boost. Memory clocks also differ, with the TITAN X at 1251 MHz (10 Gbps effective) and the A100 at 1512 MHz (3 Gbps effective). The A100’s HBM2e memory runs at a lower effective rate but compensates with a vastly wider bus and higher bandwidth.

Memory capacity is 80 GB versus 12 GB, bus width is 5120-bit versus 384-bit, and bandwidth is 1.94 TB/s versus 480.4 GB/s. The A100 has 6912 shading units, 432 TMUs, 160 ROPs, and 432 tensor cores. The TITAN X has 3584 shading units, 224 TMUs, 96 ROPs, and no tensor cores. Pixel rate is 225.6 GPixel/s versus 147.0 GPixel/s, and texture rate is 609.1 GTexel/s versus 342.9 GTexel/s.

FP32 compute is 19.49 TFLOPS versus 10.97 TFLOPS. FP16 is 77.97 TFLOPS (4:1) versus 171.5 GFLOPS (1:64), a 455-fold difference in raw FP16 throughput. TDP is 300 W for the A100 and 250 W for the TITAN X. Power connectors are 8-pin EPS versus 1x 6-pin plus 1x 8-pin. Suggested PSU is 700 W versus 600 W.

The A100 uses PCIe 4.0 x16, while the TITAN X uses PCIe 3.0 x16. The A100 has no display outputs; the TITAN X has 1x DVI, 1x HDMI 2.0, and 3x DisplayPort 1.4a. The A100’s release date is June 2021, the TITAN X’s is August 2016. Both are end-of-life, both are dual-slot, and both share the same 267 mm length, with the TITAN X being 1 mm taller and 40 mm wide.

Architecture Differences

The A100 is built on the Ampere architecture, designed for server and data center workloads. The TITAN X uses the Pascal architecture, aimed at high-end consumer graphics. These are fundamentally different designs: Ampere is compute-optimized with dedicated tensor cores (432 of them), while Pascal is a rasterization-focused GPU without any tensor core hardware.

The manufacturing process tells the story of generational advancement. The A100 uses TSMC’s 7 nm node, enabling 54,200 million transistors in an 826 mm² package. The TITAN X uses 16 nm, packing 11,800 million transistors into 471 mm². The A100’s density of 65.6M per mm² is 2.6 times higher than the TITAN X’s 25.1M per mm², allowing far more compute units per area.

Memory architecture diverges completely. The A100 employs HBM2e stacked memory on a 5120-bit interface, yielding 1.94 TB/s bandwidth. The TITAN X uses GDDR5X on a 384-bit bus, reaching 480.4 GB/s. The A100’s 80 GB capacity is nearly seven times the TITAN X’s 12 GB, making it suitable for massive models or datasets that would not fit in the older card’s frame buffer.

Compute feature sets differ sharply. The A100’s FP16 throughput of 77.97 TFLOPS (4:1) indicates native half-precision acceleration, critical for AI training. The TITAN X’s FP16 of 171.5 GFLOPS (1:64) shows it processes half-precision at a tiny fraction of its FP32 rate, making it unsuitable for modern machine learning workloads. The A100 also has 432 tensor cores, which the TITAN X lacks entirely.

The A100’s PCIe 4.0 interface doubles the bandwidth of the TITAN X’s PCIe 3.0, reducing data transfer bottlenecks. The A100’s 8-pin EPS connector and 300 W TDP reflect its server power delivery design, while the TITAN X’s dual 6-pin and 8-pin connectors suit consumer power supplies. The A100 has no display outputs, reinforcing its role as a compute-only accelerator, whereas the TITAN X retains full multi-monitor output capability.

Where Each One Wins

The A100 wins in every compute-heavy category. Its 210.5% OpenCL advantage makes it the obvious pick for general-purpose GPU computing, scientific simulation, or any workload scored by that benchmark. Its 80 GB memory capacity and 1.94 TB/s bandwidth allow it to handle datasets that would exhaust the TITAN X’s 12 GB frame buffer. The 432 tensor cores and 77.97 TFLOPS FP16 throughput give it a massive edge in AI inference and training, where the TITAN X’s 171.5 GFLOPS FP16 is effectively a non-starter.

The A100 also wins on raw throughput metrics: 19.49 TFLOPS FP32 versus 10.97 TFLOPS, 225.6 GPixel/s versus 147.0 GPixel/s, and 609.1 GTexel/s versus 342.9 GTexel/s. Its 99th percentile ranking versus the TITAN X’s 91st places it among the top accelerators in the database, while the TITAN X sits in a mid-range cluster.

The TITAN X wins only in specific, narrow scenarios. It has functional display outputs, so it can drive monitors directly, which the A100 cannot. It supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, while the A100 has no recorded API support, making the TITAN X the only option for graphics APIs. Its lower 250 W TDP and 600 W suggested PSU make it easier to integrate into consumer builds. The TITAN X also has higher base and boost clocks, though this does not translate into benchmark wins.

For users prioritizing OpenCL performance, memory capacity, FP16 compute, or tensor operations, the A100 is the only rational choice. For users needing a GPU with display output, legacy API support, or lower power requirements, the TITAN X retains limited utility, but the data shows it operates at roughly one-third the A100’s performance level. The verdict from the recorded measurements is clear: the A100 dominates the compute tier, while the TITAN X’s advantages are confined to compatibility and connectivity features.