NVIDIA A100 PCIe 40 GB vs NVIDIA A10G Comparison

The NVIDIA A100 PCIe 40 GB and the NVIDIA A10G are both Ampere-generation server accelerators, but they are engineered for distinctly different workloads. The A100 PCIe 40 GB is a data-center heavyweight built around the GA100 chip, while the A10G is a leaner, higher-clock part based on the GA102 die. Benchmark data shows the A100 PCIe 40 GB winning both recorded head-to-head tests, but the margin and the nature of those wins tell a more nuanced story about which card suits which task. The A100 PCIe 40 GB leads by 13% in Geekbench OpenCL and by a razor-thin 0.4% in Geekbench Vulkan, with an average benchmark score of 162,504 against 151,963 for the A10G.

Where Each One Wins

The A100 PCIe 40 GB is the clear winner in raw compute throughput, particularly in workloads that stress FP32 and FP16 math. Its FP32 output is 19.49 TFLOPS, and its FP16 output reaches 77.97 TFLOPS with a 4:1 ratio, which is a massive advantage for AI training and inference tasks that rely on reduced precision. The A10G counters with 31.52 TFLOPS for both FP32 and FP16 (1:1 ratio), meaning it is actually faster in FP32, but it cannot match the A100’s FP16 peak. In the Geekbench OpenCL test, which often reflects general compute, the A100 PCIe 40 GB scores 178,627 versus 158,063 for the A10G, a 13% advantage. That test favors memory bandwidth and tensor throughput, where the A100’s HBM2e memory with 1.56 TB/s bandwidth dwarfs the A10G’s 600.2 GB/s GDDR6.

The A10G wins in the domain of graphics and rasterization-heavy tasks, despite losing the Vulkan benchmark by a hair. The A10G has 9,216 shading units, 288 texture mapping units, and 96 ROPs, compared to 6,912 shading units, 432 TMUs, and 160 ROPs on the A100. While the A100 has more TMUs and ROPs, the A10G’s higher boost clock of 1710 MHz versus 1410 MHz gives it an edge in latency-sensitive, single-threaded graphics workloads. The A10G also has 72 RT cores, whereas the A100 PCIe 40 GB has none listed, making the A10G the only option for ray-traced rendering. In the Geekbench Vulkan test, the A100 wins by only 0.4% (146,380 vs 145,863), which is effectively a tie, suggesting that the A10G’s architectural advantages in shading and ray tracing nearly close the gap.

For memory-bound workloads, the A100 is the definitive victor. Its 40 GB of HBM2e on a 5120-bit bus provides 1.56 TB/s of bandwidth, which is 2.6 times the A10G’s 600.2 GB/s. This makes the A100 ideal for large language models, scientific simulations, and any dataset that exceeds 24 GB. The A10G’s 24 GB of GDDR6 is sufficient for many inference workloads but will hit capacity limits on the largest models. The data shows the A100 winning both benchmark tests, but the A10G’s strength lies in its FP32 compute and graphics feature set, not in the recorded Geekbench scores.

Architecture Differences

The two GPUs share the Ampere architecture but are built on fundamentally different silicon. The A100 PCIe 40 GB uses the GA100 chip, fabricated on a 7 nm process at TSMC, with 54,200 million transistors packed into an 826 mm² die, yielding a transistor density of 65.6 million per mm². The A10G uses the GA102 chip, built on Samsung’s 8 nm process, with 28,300 million transistors on a 628 mm² die, for a density of 45.1 million per mm². The A100’s more advanced node allows for higher transistor density, which contributes to its superior memory controller and tensor core count.

The A100 has 432 tensor cores, while the A10G has 288. This difference is critical for AI workloads, as the A100’s tensor cores are designed to deliver 77.97 TFLOPS in FP16, while the A10G’s tensor cores peak at 31.52 TFLOPS in FP16 (1:1). The A100 also features a 5120-bit memory bus, which is over 13 times wider than the A10G’s 384-bit bus. This is a direct result of the HBM2e memory stack versus GDDR6, and it explains the massive bandwidth gap. The A10G compensates with higher clocks: a base of 1320 MHz and boost of 1710 MHz, versus 765 MHz base and 1410 MHz boost on the A100. This clock advantage allows the A10G to achieve higher FP32 throughput (31.52 TFLOPS vs 19.49 TFLOPS) despite having fewer tensor cores.

The A10G is the only one of the two with RT cores (72), making it the sole choice for ray-traced workloads. It also has full API support, including DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, while the A100 lists no API support in the data. The A100 has no display outputs, and neither card does, confirming their server-oriented design. Power and physical design differ significantly: the A100 is a dual-slot card with a 250 W TDP and a suggested 600 W power supply, while the A10G is a single-slot card with a 150 W TDP and a 450 W suggested PSU. Both use an 8-pin EPS power connector and measure approximately 267 mm in length, but the A100 is 111 mm tall versus 112 mm for the A10G.

The Verdict

The data points to a clear split: the NVIDIA A100 PCIe 40 GB is the superior choice for compute-heavy, memory-intensive, and AI-focused workloads, while the NVIDIA A10G is the better fit for graphics, ray tracing, and FP32-heavy tasks. In the head-to-head benchmarks, the A100 wins both tests, but the margins reveal the story. The 13% OpenCL win is substantial and reflects the A100’s memory bandwidth and tensor core advantage. The 0.4% Vulkan win is negligible, meaning the A10G is effectively on par in graphics-oriented tests, thanks to its higher clocks and RT cores.

For a user training large neural networks or processing datasets that exceed 24 GB, the A100’s 40 GB HBM2e and 1.56 TB/s bandwidth are non-negotiable. Its 77.97 TFLOPS FP16 performance is more than double the A10G’s 31.52 TFLOPS, making it the only rational choice for deep learning. Conversely, for a user running real-time rendering with ray tracing, the A10G’s 72 RT cores and DirectX 12 Ultimate support make it the only option, as the A100 has no RT cores and no API support listed. The A10G also consumes 100 W less power (150 W vs 250 W) and occupies a single slot, which is a practical advantage in dense server configurations.

The average benchmark score of 162,504 for the A100 places it at the 97th percentile of all GPUs, with its nearest rival being the NVIDIA RTX 4500 Ada Generation at 166,094 (a 2.2% gap). The A10G also sits at the 97th percentile with an average score of 151,963, but its nearest rival is the NVIDIA Tesla V100 PCIe 32 GB at 150,305 (a 1.1% gap). This indicates that the A100 is positioned higher in the absolute performance hierarchy, while the A10G is closer to the previous-generation data-center parts. For anyone who needs maximum compute and memory, the A100 is the verdict. For anyone who needs a versatile, lower-power card with ray tracing and FP32 speed, the A10G is the answer.

FAQ

Q: Which GPU has higher FP32 performance?

A: The NVIDIA A10G, with 31.52 TFLOPS, is significantly higher than the A100 PCIe 40 GB’s 19.49 TFLOPS.

Q: Which GPU has more memory bandwidth?

A: The NVIDIA A100 PCIe 40 GB, with 1.56 TB/s, is more than double the A10G’s 600.2 GB/s.

Q: Does the A100 support ray tracing?

A: No. The A100 PCIe 40 GB has no RT cores listed, while the A10G has 72 RT cores.

Q: What is the power consumption difference?

A: The A100 PCIe 40 GB has a 250 W TDP, while the A10G has a 150 W TDP, a difference of 100 W.

Q: Which GPU won the Geekbench Vulkan test?

A: The NVIDIA A100 PCIe 40 GB won with a score of 146,380, but only by 0.4% over the A10G’s 145,863.

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

A: The A100 PCIe 40 GB has an average score of 162,504, while the A10G has an average score of 151,963.

Head-to-Head Benchmarks

The first head-to-head test is Geekbench OpenCL, where the A100 PCIe 40 GB scores 178,627 against the A10G’s 158,063. This is a 13% delta in favor of the A100. This result aligns with the memory and tensor core differences: the A100’s 1.56 TB/s bandwidth and 432 tensor cores allow it to process large parallel workloads far more efficiently than the A10G’s 600.2 GB/s and 288 tensor cores. The OpenCL test often stresses memory throughput and compute shaders, areas where the A100’s HBM2e excels.

The second test is Geekbench Vulkan, where the A100 scores 146,380 and the A10G scores 145,863. The delta is just 0.4%, making this effectively a statistical tie. This is surprising given the A100’s overall compute advantage, but it reflects the A10G’s superior clock speeds (1710 MHz boost vs 1410 MHz) and its 9,216 shading units, which outnumber the A100’s 6,912. The A10G also has 72 RT cores, which, while not directly tested in Vulkan, contribute to its graphics pipeline efficiency. The A100’s win here is marginal, suggesting that for graphics-oriented Vulkan workloads, the two cards are interchangeable in performance.

Overall, the A100 wins both head-to-head tests, but the OpenCL win is decisive (13%) while the Vulkan win is negligible (0.4%). The data indicates that the A100 is the compute king, while the A10G holds its own in graphics. The wins tally is 2 for the A100 and 0 for the A10G, but the Vulkan result shows that the A10G is not far behind in that specific arena.

Specification Differences

The two cards differ in nearly every core specification. The A100 PCIe 40 GB uses the GA100 chip with 54,200 million transistors on an 826 mm² die, while the A10G uses the GA102 chip with 28,300 million transistors on a 628 mm² die. The process nodes differ: 7 nm TSMC for the A100 versus 8 nm Samsung for the A10G. Transistor density is 65.6 million per mm² for the A100 and 45.1 million per mm² for the A10G.

Clock speeds are starkly different. The A100 has a base clock of 765 MHz and a boost of 1410 MHz, while the A10G runs at 1320 MHz base and 1710 MHz boost. Memory configurations are also divergent: the A100 offers 40 GB of HBM2e on a 5120-bit bus with 1.56 TB/s bandwidth, whereas the A10G has 24 GB of GDDR6 on a 384-bit bus with 600.2 GB/s bandwidth. The A100 has 6,912 shading units, 432 TMUs, and 160 ROPs, while the A10G has 9,216 shading units, 288 TMUs, and 96 ROPs.

The A100 has 432 tensor cores and no RT cores, while the A10G has 288 tensor cores and 72 RT cores. Pixel rate is 225.6 GPixel/s for the A100 and 164.2 GPixel/s for the A10G, while texture rate is 609.1 GTexel/s for the A100 and 492.5 GTexel/s for the A10G. FP32 performance is 19.49 TFLOPS for the A100 and 31.52 TFLOPS for the A10G. FP16 performance is 77.97 TFLOPS (4:1) for the A100 and 31.52 TFLOPS (1:1) for the A10G. The A100 has a 250 W TDP and is dual-slot, while the A10G has a 150 W TDP and is single-slot. Both use an 8-pin EPS connector, but the A100 suggests a 600 W PSU while the A10G suggests 450 W. The A10G supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the A100 lists no API support. Both cards have no display outputs and are end-of-life, with the A100 released in June 2020 and the A10G in April 2021.