NVIDIA A10M vs NVIDIA Quadro GP100 Comparison

Head-to-Head Benchmarks

The recorded data contains a single direct comparison between the NVIDIA A10M and the NVIDIA Quadro GP100: the Geekbench OpenCL test. In this measurement, the A10M scores 135230 points, while the Quadro GP100 scores 87445 points. This gives the A10M a decisive 54.6% advantage, marking a one-sided head-to-head result with the A10M taking the sole win and the GP100 recording zero wins.

This margin is substantial. A 54.6% delta means the A10M delivers over half again as much raw compute throughput in this workload. Such a gap suggests not just incremental improvement but a generational leap in raw execution capability. The A10M's score also places it in the 96th percentile among all GPUs in the database, while the Quadro GP100 sits at the 93rd percentile. Both are high performers, but the A10M is clearly operating in a higher tier.

Looking at the nearest rivals for each card helps contextualize these numbers. The A10M's closest competitor is the NVIDIA RTX 4000 Ada Generation with an average score of 135218, a negligible 0% difference. The AMD Radeon PRO W6800 scores 135396, which is 0.1% ahead of the A10M. Two other AMD cards, the Radeon Pro W6800X Duo and Radeon PRO V620, are 0.4% and 0.9% ahead respectively. This tells us the A10M is right at the top of its performance neighborhood, trading blows with the fastest workstation cards in the database.

For the Quadro GP100, the picture is different. Its nearest rival is the AMD Radeon PRO W7600 with a score of 87108, which is 0.4% behind the GP100. The NVIDIA CMP 40HX trails by 2.1% with 85637 points. On the other side, the NVIDIA RTX A4500 Mobile is 4% ahead with 91134 points, and the desktop RTX A4500 is 4.6% ahead with 91671 points. The GP100 is therefore competitive within its own generation, but it sits noticeably below the current generation of workstation parts.

The benchmark results indicate that in OpenCL compute, the A10M is not merely faster; it is in a completely different performance class. The 54.6% lead is the kind of margin that changes workflow decisions, as it means the A10M can handle substantially larger datasets or more complex simulations within the same time frame.

FAQ

Q: How much faster is the NVIDIA A10M than the NVIDIA Quadro GP100 in OpenCL?

A: The A10M scores 135230 in Geekbench OpenCL, while the Quadro GP100 scores 87445. This gives the A10M a 54.6% advantage.

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

A: The A10M ranks in the 96th percentile, while the Quadro GP100 ranks in the 93rd percentile.

Q: What are the closest competitors to the A10M?

A: The RTX 4000 Ada Generation (135218, 0% delta), AMD Radeon PRO W6800 (135396, 0.1% ahead), AMD Radeon Pro W6800X Duo (135774, 0.4% ahead), and AMD Radeon PRO V620 (136472, 0.9% ahead).

Q: What are the closest competitors to the Quadro GP100?

A: The AMD Radeon PRO W7600 (87108, 0.4% behind), NVIDIA CMP 40HX (85637, 2.1% behind), NVIDIA RTX A4500 Mobile (91134, 4% ahead), and NVIDIA RTX A4500 (91671, 4.6% ahead).

Q: Does the Quadro GP100 win any benchmark against the A10M?

A: No, the recorded data shows the A10M winning the sole head-to-head benchmark, with the GP100 recording zero wins.

Q: What is the memory configuration of each card?

A: The A10M has 20 GB of GDDR6 memory on a 320-bit bus with 500.2 GB/s bandwidth. The Quadro GP100 has 16 GB of HBM2 memory on a 4096-bit bus with 732.2 GB/s bandwidth.

Architecture Differences

The architectural divide between these two GPUs is fundamental. The A10M uses the GA102 chip built on the Ampere architecture, fabricated on an 8 nm process at Samsung. The Quadro GP100 uses the GP100 chip on the older Pascal architecture, built on a 16 nm process at TSMC. This process node difference is significant: the A10M packs 28,300 million transistors into a 628 mm² die, achieving a transistor density of 45.1 million per square millimeter. The GP100 contains 15,300 million transistors on a 610 mm² die, with a density of 25.1 million per square millimeter. The A10M nearly doubles the transistor count while using a slightly larger die, and its density advantage is roughly 80% higher.

The shading unit counts reflect this architectural evolution. The A10M has 7168 shading units, exactly double the GP100's 3584. Both have 224 texture mapping units, but the A10M has 80 raster operation units versus the GP100's 96. This is an interesting inversion: the older card has more ROPs despite having far fewer shaders.

The most dramatic difference lies in specialized hardware. The A10M includes 56 ray tracing cores and 224 tensor cores, features that are entirely absent from the Quadro GP100. This means the A10M supports hardware-accelerated ray tracing and tensor operations, while the GP100 must rely on compute shaders for any such workloads. The A10M also supports DirectX 12 Ultimate (12_2), whereas the GP100 is limited to DirectX 12 (12_1). Both support OpenGL 4.6, but the A10M has Vulkan 1.4 support compared to the GP100's Vulkan 1.3.

Clock speeds tell an interesting story. The GP100 has a higher base clock at 1304 MHz versus the A10M's 975 MHz, but the A10M has a higher boost clock at 1635 MHz versus 1443 MHz. The A10M's memory runs at 1563 MHz (12.5 Gbps effective) compared to the GP100's 715 MHz (1430 Mbps effective). However, the GP100's HBM2 memory uses a massive 4096-bit bus, giving it 732.2 GB/s bandwidth, which is notably higher than the A10M's 500.2 GB/s from its 320-bit GDDR6 interface.

Specification Differences

The two cards differ across nearly every major specification category. The A10M uses a newer 8 nm process versus the GP100's 16 nm. Transistor counts are 28,300 million versus 15,300 million. Die size is comparable at 628 mm² versus 610 mm², but transistor density is 45.1M/mm² versus 25.1M/mm².

Memory configurations diverge sharply: the A10M has 20 GB of GDDR6 on a 320-bit bus, while the GP100 has 16 GB of HBM2 on a 4096-bit bus. Bandwidth favors the GP100 at 732.2 GB/s versus 500.2 GB/s. The A10M has 7168 shading units against 3584, 224 TMUs on both, and 80 ROPs versus 96. Ray tracing cores (56) and tensor cores (224) exist only on the A10M.

Compute throughput is heavily skewed. The A10M delivers 23.44 TFLOPS FP32 versus 10.34 TFLOPS. For FP16, the A10M again delivers 23.44 TFLOPS (1:1 ratio), while the GP100 produces 20.69 TFLOPS (2:1 ratio). Pixel rate is slightly higher on the GP100 at 138.5 GPixel/s versus 130.8 GPixel/s, but texture rate favors the A10M at 366.2 GTexel/s versus 323.2 GTexel/s.

Power and physical specifications also differ. The A10M has a 150 W TDP, is single-slot, and uses an 8-pin EPS connector with a 450 W suggested PSU. The GP100 has a 235 W TDP, is dual-slot, uses a single 8-pin connector, and suggests a 550 W PSU. The A10M uses PCIe 4.0 x16, while the GP100 uses PCIe 3.0 x16. The A10M has no display outputs, whereas the GP100 offers 1x DVI and 4x DisplayPort 1.4a. Both are 267 mm long, with the A10M at 112 mm height and the GP100 at 111 mm.

Where Each One Wins

The A10M wins in raw compute performance, as demonstrated by its 54.6% lead in OpenCL. It also wins on memory capacity with 20 GB versus 16 GB, on FP32 throughput with more than double the TFLOPS, and on feature support with ray tracing and tensor cores. Its lower TDP of 150 W versus 235 W means it achieves higher performance while consuming less power, a significant efficiency advantage. The PCIe 4.0 interface doubles the interconnect bandwidth available to the A10M.

The Quadro GP100 wins in memory bandwidth, offering 732.2 GB/s versus 500.2 GB/s, which could benefit certain memory-bound workloads despite the lower compute throughput. It also has more ROPs (96 versus 80) and a higher pixel rate (138.5 GPixel/s versus 130.8 GPixel/s), potentially giving it an edge in fill-rate-limited scenarios. Its display outputs (1x DVI, 4x DisplayPort) make it usable for direct display connection, while the A10M has no outputs at all. The GP100 also has a higher base clock (1304 MHz versus 975 MHz), though the A10M's boost clock is higher.

The Verdict

The data points to a clear conclusion: the NVIDIA A10M is the superior GPU for compute-intensive workloads. Its 54.6% OpenCL lead is decisive, and its additional features like ray tracing and tensor cores expand its utility beyond what the Quadro GP100 can offer. The A10M also achieves this with a lower TDP (150 W versus 235 W), making it more efficient in both performance and power consumption.

For professionals running OpenCL-based applications, simulations, or machine learning tasks, the A10M is the obvious choice based on the benchmark data. Its 20 GB memory capacity also provides more headroom for large datasets. The lack of display outputs is a limitation, but for server or compute deployments where the GPU is not driving a display, this is irrelevant.

The Quadro GP100 retains relevance in specific scenarios. Its higher memory bandwidth and larger ROP count could make it competitive for certain graphics-heavy or bandwidth-sensitive tasks. The presence of display outputs means it can serve in workstation roles requiring direct video output. However, its lower compute scores and lack of modern features like tensor and ray tracing cores place it firmly in the previous generation.

The recorded data shows one winner across the board: the A10M takes the sole head-to-head benchmark, holds a higher percentile ranking (96th versus 93rd), and offers more than double the FP32 throughput. For anyone choosing between these two based on performance metrics, the A10M is the clear recommendation. The GP100's advantages are narrower and more specialized, making it a niche choice for specific bandwidth-sensitive or display-centric use cases where its unique memory architecture and output capabilities matter more than raw compute.