NVIDIA A10G vs NVIDIA Quadro GP100 Comparison

Head-to-Head Benchmarks

The recorded database contains a single direct comparison between the NVIDIA A10G and the NVIDIA Quadro GP100, the Geekbench OpenCL test. The NVIDIA A10G posts a score of 158063, while the NVIDIA Quadro GP100 records 87445. The A10G wins decisively, with a delta of 80.8% over the GP100. This is not a marginal victory; it is a near-doubling of the raw compute output in a general-purpose GPU workload. The A10G's result places it at the 97th percentile of all GPUs in the database, while the GP100 sits at the 93rd percentile, a gap that underscores how much the older card has fallen behind modern server accelerators.

Context from the nearest rivals reinforces the A10G's standing. Its average benchmark score is 151963, which puts it 1.1% ahead of the NVIDIA Tesla V100 PCIe 32 GB (150305) and 9.3% ahead of the AMD Instinct MI100 (139035). However, it trails the AMD Radeon Pro W6800X by 5.4% (160671) and the NVIDIA A100 PCIe 40 GB by 6.5% (162504). So while the A10G beats the GP100 by a wide margin, it sits in a competitive mid-tier among contemporary accelerators, close to the V100 but clearly behind the A100. The Quadro GP100, by contrast, has an average score of 87445, which places it just 0.4% ahead of the AMD Radeon PRO W7600 (87108) and 2.1% ahead of the NVIDIA CMP 40HX (85637), but 4.0% behind the NVIDIA RTX A4500 Mobile (91134) and 4.6% behind the NVIDIA RTX A4500 (91671). The GP100 is effectively a lower-mid-range part in the current database landscape, whereas the A10G operates near the top of the compute hierarchy.

The single head-to-head test is the only direct evidence, but it is consistent with the broader specification gap between the two cards. The A10G's OpenCL score of 158063 versus the GP100's 87445 reflects not just a generational leap but also a fundamental difference in compute philosophy, which the architecture section will explore.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA A10G has an average benchmark score of 151963, compared to the NVIDIA Quadro GP100's 87445. The A10G is 80.8% faster in the head-to-head OpenCL test.

Q: How does the A10G compare to its nearest rivals?

A: The A10G is 1.1% ahead of the NVIDIA Tesla V100 PCIe 32 GB and 9.3% ahead of the AMD Instinct MI100. It trails the AMD Radeon Pro W6800X by 5.4% and the NVIDIA A100 PCIe 40 GB by 6.5%.

Q: Where does the Quadro GP100 rank among its peers?

A: The GP100 is 0.4% ahead of the AMD Radeon PRO W7600 and 2.1% ahead of the NVIDIA CMP 40HX, but it is 4.0% behind the NVIDIA RTX A4500 Mobile and 4.6% behind the NVIDIA RTX A4500.

Q: What is the memory configuration difference?

A: The A10G uses 24 GB of GDDR6 on a 384-bit bus, delivering 600.2 GB/s of bandwidth. The GP100 uses 16 GB of HBM2 on a 4096-bit bus, delivering 732.2 GB/s of bandwidth. The GP100 has higher bandwidth despite less capacity.

Q: Which card has a higher FP32 compute rating?

A: The A10G is rated at 31.52 TFLOPS FP32, while the GP100 is rated at 10.34 TFLOPS FP32. The A10G is roughly three times higher in single-precision throughput.

Q: Do both cards support the same API levels?

A: No. The A10G supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the GP100 supports DirectX 12 (12_1) and Vulkan 1.3. Both support OpenGL 4.6.

Architecture Differences

The architectural divide is stark. The NVIDIA A10G is built on the GA102 chip using the Ampere architecture, fabricated on an 8 nm process at Samsung. It packs 28,300 million transistors into a 628 mm² die, yielding a transistor density of 45.1 million per square millimeter. The NVIDIA Quadro GP100 uses the GP100 chip with the older Pascal architecture, built on a 16 nm process at TSMC. It contains 15,300 million transistors on a 610 mm² die, with a density of 25.1 million per square millimeter. The A10G nearly doubles the transistor count while keeping a similar die size, which explains its massive compute advantage.

The A10G is a server Ampere part with 9216 shading units, 288 texture mapping units, and 96 render output units. It also includes 72 ray tracing cores and 288 tensor cores, making it a fully featured accelerator for modern graphics and AI workloads. The GP100, by contrast, has 3584 shading units, 224 TMUs, and 96 ROPs, with no ray tracing cores and no tensor cores. This is a fundamental difference: the A10G can accelerate ray-traced rendering and tensor operations in hardware, while the GP100 relies entirely on traditional shader compute. The absence of tensor cores on the GP100 also means it lacks the dedicated matrix math hardware that dominates modern deep learning inference and training.

Clock behavior differs as well. The A10G has a base clock of 1320 MHz and a boost clock of 1710 MHz, while the GP100 has a base of 1304 MHz and a boost of 1443 MHz. The A10G boosts significantly higher, and with more than double the shading units, its pixel rate reaches 164.2 GPixel/s and its texture rate 492.5 GTexel/s. The GP100 manages 138.5 GPixel/s and 323.2 GTexel/s. The A10G's FP32 throughput is 31.52 TFLOPS, while its FP16 rate is identical at 31.52 TFLOPS due to a 1:1 ratio. The GP100's FP32 is 10.34 TFLOPS, but its FP16 jumps to 20.69 TFLOPS thanks to a 2:1 ratio. Even with that FP16 advantage, the A10G still delivers more than 1.5 times the half-precision throughput.

Process node differences also affect power characteristics. The A10G is rated at 150 W TDP, while the GP100 is rated at 235 W. The A10G achieves far higher performance at lower power, a direct result of the 8 nm Samsung process versus the older 16 nm TSMC node.

Specification Differences

The two cards differ across nearly every major specification category. The A10G uses an 8 nm process from Samsung; the GP100 uses 16 nm from TSMC. Transistor counts are 28,300 million versus 15,300 million, and transistor density is 45.1M per mm² versus 25.1M per mm². Die size is close, 628 mm² for the A10G versus 610 mm² for the GP100.

Memory is a major differentiator. The A10G has 24 GB of GDDR6 on a 384-bit bus with 600.2 GB/s bandwidth. The GP100 has 16 GB of HBM2 on a 4096-bit bus with 732.2 GB/s bandwidth. The GP100 wins on bandwidth, the A10G on capacity and memory technology generation.

Compute resources differ sharply. The A10G has 9216 shading units, 288 TMUs, and 96 ROPs, plus 72 ray tracing cores and 288 tensor cores. The GP100 has 3584 shading units, 224 TMUs, and 96 ROPs, with no ray tracing or tensor cores. FP32 is 31.52 TFLOPS for the A10G versus 10.34 TFLOPS for the GP100. FP16 is 31.52 TFLOPS (1:1) for the A10G versus 20.69 TFLOPS (2:1) for the GP100. Pixel rate is 164.2 GPixel/s versus 138.5 GPixel/s, and texture rate is 492.5 GTexel/s versus 323.2 GTexel/s.

Power and physical design also diverge. The A10G has a 150 W TDP and is single-slot with an 8-pin EPS connector and a suggested PSU of 450 W. The GP100 has a 235 W TDP, is dual-slot, uses a single 8-pin connector, and suggests a 550 W PSU. The A10G uses PCIe 4.0 x16, while the GP100 uses PCIe 3.0 x16. The A10G has no display outputs, whereas the GP100 provides 1x DVI and 4x DisplayPort 1.4a. API support differs: the A10G supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the GP100 supports DirectX 12 (12_1) and Vulkan 1.3. Both support OpenGL 4.6. Dimensions are nearly identical, with both cards at 267 mm in length, though the A10G is 112 mm high and the GP100 is 111 mm high.

Where Each One Wins

The NVIDIA A10G wins in every compute-heavy scenario. Its 80.8% lead in the OpenCL head-to-head makes it the clear choice for general-purpose GPU compute, FP32 workloads, and any application that can leverage its tensor cores for AI or machine learning. The 31.52 TFLOPS FP32 rating is three times the GP100's 10.34 TFLOPS, and the presence of 288 tensor cores means the A10G can accelerate matrix operations that the GP100 cannot handle in hardware at all. For rendering, the A10G's 72 ray tracing cores provide hardware-accelerated ray tracing, something the GP100 lacks entirely. The A10G's higher boost clock of 1710 MHz versus 1443 MHz, combined with more shading units, gives it a decisive edge in pixel and texture throughput: 164.2 GPixel/s and 492.5 GTexel/s versus 138.5 GPixel/s and 323.2 GTexel/s. Its lower TDP of 150 W versus 235 W also makes it more attractive for dense server deployments.

The Quadro GP100 does have specific advantages, though they are narrower. Its HBM2 memory provides 732.2 GB/s of bandwidth, which is 22% higher than the A10G's 600.2 GB/s. For memory-bound workloads that fit within 16 GB, such as certain scientific simulations or large data transfers, the GP100 could theoretically hold an advantage, though the A10G's far higher compute throughput would likely dominate in most integrated workloads. The GP100 also has display outputs, including 1x DVI and 4x DisplayPort 1.4a, making it usable in workstation environments where a direct display connection is required. The A10G has no display outputs, so it is strictly a compute or server accelerator. The GP100's PCIe 3.0 interface is older, but it remains functional for legacy systems that do not support PCIe 4.0.

In terms of database rankings, the A10G sits at the 97th percentile of all GPUs, while the GP100 sits at the 93rd. That four-percentile gap reflects the A10G's placement among modern accelerators, close to the Tesla V100 and A100, while the GP100 competes with mid-range workstation cards like the RTX A4500. For any new deployment, the A10G is the superior choice on raw performance, features, and power efficiency. The GP100's only practical wins are memory bandwidth and display connectivity, both of which are secondary in a server compute context.