NVIDIA A10G vs NVIDIA Tesla V100 SXM2 32 GB Comparison

The NVIDIA A10G and NVIDIA Tesla V100 SXM2 32 GB are both end-of-life server accelerators, but they serve fundamentally different workloads. The data shows a clear split: the A10G is the modern, compute-dense option with a decisive lead in raw compute benchmarks, while the V100 SXM2 retains relevance through its massive 32 GB HBM2 memory pool and unique architectural traits. The A10G wins both head-to-head tests, but the V100 SXM2 is not obsolete—it is a specialist tool for memory-bound tasks.

Where Each One Wins

The A10G wins outright in the two available benchmark tests, taking the Geekbench OpenCL and Vulkan workloads. Its 20% lead in OpenCL is the headline result, indicating a substantial advantage in general-purpose compute and GPU-accelerated tasks. The Vulkan win is narrower—just 1.5%—suggesting that in graphics-adjacent or API-specific workloads, the gap nearly closes. This pattern points to the A10G being the better all-rounder for modern compute environments, especially those leveraging FP32 throughput or newer API features.

The V100 SXM2, by contrast, does not win any benchmark in this comparison. However, its strength lies outside these synthetic tests. It offers 32 GB of HBM2 memory with 898.0 GB/s of bandwidth, which is 33% more memory capacity and roughly 50% more bandwidth than the A10G’s 24 GB GDDR6 at 600.2 GB/s. In workloads that are memory-capacity-bound or bandwidth-starved—such as large model inference, data analytics, or scientific simulations—the V100 SXM2 will outperform the A10G despite losing in generic compute benchmarks. The data does not measure this directly, but the specification difference is stark and decisive for those use cases.

The A10G also wins on architectural modernity. Its Ampere architecture supports DirectX 12 Ultimate (12_2), while the V100 SXM2 is limited to DirectX 12 (12_1). The A10G’s 72 RT cores provide hardware ray tracing capability, which the V100 SXM2 completely lacks. For any workload leveraging ray tracing or the latest graphics features, the A10G is the only choice.

Architecture Differences

The A10G is built on NVIDIA’s Ampere architecture using a GA102 chip fabricated on Samsung’s 8 nm process. It packs 28,300 million transistors into a 628 mm² die, yielding a transistor density of 45.1M per mm². The V100 SXM2 uses the older Volta architecture with a GV100 chip on TSMC’s 12 nm process. It contains 21,100 million transistors on a larger 815 mm² die, resulting in a much lower density of 25.9M per mm². The A10G’s smaller, denser process is a key reason it achieves higher clock speeds and better compute efficiency.

Core counts differ dramatically. The A10G has 9,216 shading units, 288 TMUs, and 96 ROPs, while the V100 SXM2 has just 5,120 shading units, 320 TMUs, and 128 ROPs. The A10G has nearly double the shading units, but the V100 SXM2 counters with more TMUs and ROPs, giving it higher pixel fill rate (195.8 GPixel/s vs 164.2 GPixel/s) and nearly identical texture rate (489.6 GTexel/s vs 492.5 GTexel/s). This means the V100 SXM2 is not simply slower—it is tuned for different operations.

Tensor core counts also differ: the A10G has 288 tensor cores, while the V100 SXM2 has 640. However, these are different generations of tensor cores, and the V100’s higher count does not translate to a benchmark win. The A10G’s FP32 throughput is 31.52 TFLOPS, exactly double the V100 SXM2’s 15.67 TFLOPS. Both achieve similar FP16 performance (31.52 TFLOPS vs 31.33 TFLOPS), but the A10G does it with a 1:1 ratio, while the V100 uses a 2:1 ratio, indicating a different compute path.

Memory architecture is the biggest divergence. The A10G uses 24 GB of GDDR6 on a 384-bit bus, while the V100 SXM2 uses 32 GB of HBM2 on a 4096-bit bus. The V100’s wider bus and HBM2 technology deliver 898.0 GB/s versus the A10G’s 600.2 GB/s. Clock speeds favor the A10G: its base is 1320 MHz and boost is 1710 MHz, compared to the V100’s 1290 MHz base and 1530 MHz boost. The A10G also runs memory at 1563 MHz (12.5 Gbps effective) versus the V100’s 877 MHz (1754 Mbps effective).

Head-to-Head Benchmarks

The Geekbench OpenCL test shows the A10G with a score of 158,063 against the V100 SXM2’s 131,696. This is a 20% delta, the largest gap in the comparison. The A10G’s double FP32 throughput and higher boost clock are the likely drivers, making it the clear choice for OpenCL-based compute workloads. This result aligns with the A10G’s average benchmark score of 151,963 versus the V100’s 137,731, a 10% overall advantage.

The Vulkan test is much closer: the A10G scores 145,863 while the V100 SXM2 scores 143,765, a delta of only 1.5%. This near-tie suggests that Vulkan workloads are less sensitive to raw FP32 power and more dependent on other factors like memory bandwidth or driver optimization. The V100 SXM2’s higher texture rate and pixel rate may help it keep pace in this API, but it still loses. The A10G’s 72 RT cores and DirectX 12 Ultimate support do not directly affect Vulkan scores, but they indicate the A10G is better equipped for future workloads.

Looking at percentile rankings, the A10G sits in the 97th percentile of all GPUs, while the V100 SXM2 is in the 96th. This is a narrow margin, but it reflects the A10G’s edge in average benchmark score. The A10G’s nearest rival is the NVIDIA Tesla V100 PCIe 32 GB, which scores 150,305 with a delta of 1.1%—meaning the A10G beats that card too. The V100 SXM2’s nearest rival is the NVIDIA Tesla V100 PCIe 16 GB at 138,063, a delta of -0.2%, indicating the SXM2 is essentially tied with its PCIe sibling.

The Verdict

The data is unambiguous: the NVIDIA A10G wins both head-to-head benchmarks and offers superior compute density, newer architecture, and higher clock speeds. Anyone running OpenCL or Vulkan workloads should choose the A10G, as it delivers 20% better performance in the former and a measurable edge in the latter. Its 97th percentile ranking and average score of 151,963 confirm it is the stronger overall performer.

The NVIDIA Tesla V100 SXM2 32 GB is not without merit, but its appeal is narrow. Its 32 GB of HBM2 memory with 898.0 GB/s bandwidth is a unique asset that the A10G cannot match. For workloads that are memory-capacity-bound—such as large language model inference, scientific computing with massive datasets, or high-resolution simulations—the V100 SXM2 is the better choice despite losing in synthetic benchmarks. Its 96th percentile ranking and 137,731 average score show it remains competitive, but only in specific niches.

For most users, the A10G is the correct pick. It is faster, cooler (150 W TDP vs 250 W), and requires a less demanding PSU (450 W vs 600 W). It also supports PCIe 4.0, while the V100 SXM2 is limited to PCIe 3.0. The A10G’s single-slot form factor and 8-pin EPS connector make it easier to deploy than the SXM module, which has no power connector and requires a proprietary chassis. The verdict is straightforward: choose the A10G for compute performance and modern features, choose the V100 SXM2 only if memory capacity or bandwidth is the absolute priority.

FAQ

Q: Which GPU has higher FP32 performance?

A: The NVIDIA A10G delivers 31.52 TFLOPS of FP32, exactly double the Tesla V100 SXM2’s 15.67 TFLOPS.

Q: Does the Tesla V100 SXM2 have more memory than the A10G?

A: Yes, the V100 SXM2 has 32 GB of HBM2, while the A10G has 24 GB of GDDR6.

Q: What is the memory bandwidth difference?

A: The V100 SXM2 provides 898.0 GB/s over a 4096-bit bus, compared to the A10G’s 600.2 GB/s over a 384-bit bus.

Q: Which GPU supports ray tracing?

A: The A10G has 72 RT cores and supports DirectX 12 Ultimate (12_2). The V100 SXM2 has no RT cores and is limited to DirectX 12 (12_1).

Q: How do the average benchmark scores compare?

A: The A10G averages 151,963, placing it in the 97th percentile. The V100 SXM2 averages 137,731, placing it in the 96th percentile.

Q: What is the power requirement difference?

A: The A10G has a 150 W TDP and recommends a 450 W PSU. The V100 SXM2 has a 250 W TDP and recommends a 600 W PSU.

Specification Differences

| Specification | NVIDIA A10G | NVIDIA Tesla V100 SXM2 32 GB |

|----------------|-------------|------------------------------|

| Architecture | Ampere | Volta |

| Process Node | 8 nm (Samsung) | 12 nm (TSMC) |

| Transistors | 28,300 million | 21,100 million |

| Die Size | 628 mm² | 815 mm² |

| Transistor Density | 45.1M / mm² | 25.9M / mm² |

| Base Clock | 1320 MHz | 1290 MHz |

| Boost Clock | 1710 MHz | 1530 MHz |

| Memory Size | 24 GB GDDR6 | 32 GB HBM2 |

| Memory Bus | 384 bit | 4096 bit |

| Memory Bandwidth | 600.2 GB/s | 898.0 GB/s |

| Shading Units | 9,216 | 5,120 |

| TMUs | 288 | 320 |

| ROPs | 96 | 128 |

| RT Cores | 72 | None |

| Tensor Cores | 288 | 640 |

| FP32 Performance | 31.52 TFLOPS | 15.67 TFLOPS |

| FP16 Performance | 31.52 TFLOPS (1:1) | 31.33 TFLOPS (2:1) |

| Pixel Rate | 164.2 GPixel/s | 195.8 GPixel/s |

| Texture Rate | 492.5 GTexel/s | 489.6 GTexel/s |

| TDP | 150 W | 250 W |

| Slot Width | Single-slot | SXM Module |

| Power Connector | 8-pin EPS | None |

| Suggested PSU | 450 W | 600 W |

| Bus Interface | PCIe 4.0 x16 | PCIe 3.0 x16 |

| DirectX Support | 12 Ultimate (12_2) | 12 (12_1) |

| Release Date | 2021-04-11 | 2018-03-26 |