NVIDIA A100 SXM4 80 GB vs NVIDIA A10G Comparison

The NVIDIA A100 SXM4 80 GB and the NVIDIA A10G are both Ampere-generation server accelerators, but they are engineered for fundamentally different workloads. The A100 SXM4 80 GB is a high-bandwidth, HBM2e-equipped compute monster designed for large-scale AI and HPC, while the A10G is a lower-power, GDDR6-based single-slot card aimed at inference and virtualized environments. Benchmark data from Geekbench shows a decisive 26% Vulkan performance advantage for the A100, placing it in the 98th percentile versus the A10G's 97th. However, the A10G counters with a significantly higher FP32 throughput and a dramatically lower power envelope, making the choice dependent on whether raw compute density or deployment flexibility is the priority.

FAQ

Q: Which GPU is faster in the available Geekbench Vulkan benchmark?

A: The NVIDIA A100 SXM4 80 GB scores 183,725, which is 26% higher than the A10G's 145,863 in the same test. This places the A100 in the 98th percentile of all GPUs, while the A10G sits at the 97th percentile.

Q: How do the memory subsystems compare?

A: The A100 uses 80 GB of HBM2e across a 5120-bit bus, delivering 2.04 TB/s of bandwidth. The A10G uses 24 GB of GDDR6 on a 384-bit bus, providing 600.2 GB/s. This represents a 3.4x bandwidth advantage for the A100.

Q: Which card has higher raw FP32 compute?

A: The A10G leads in FP32, delivering 31.52 TFLOPS compared to the A100's 19.49 TFLOPS. This is a 61.7% advantage for the A10G in single-precision floating-point performance.

Q: What are the power requirements for each?

A: The A100 has a 400 W TDP with a suggested 800 W PSU, while the A10G has a 150 W TDP with a suggested 450 W PSU. The A10G consumes 62.5% less power and requires a significantly smaller power supply.

Q: Which GPU supports more advanced graphics APIs?

A: The A10G supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The A100 lists no API support for DirectX, OpenGL, or Vulkan in the data, indicating its focus on compute rather than graphics.

Q: How do the nearest rivals compare for each card?

A: The A100's closest rival is the NVIDIA RTX 5000 Ada Generation, which scores 184,664, just 0.5% higher. The A10G's nearest rival is the NVIDIA Tesla V100 PCIe 32 GB, which scores 150,305, a 1.1% difference.

Architecture Differences

The two GPUs share the Ampere architecture but are built on different silicon and process nodes. The A100 uses the GA100 chip fabricated on TSMC's 7 nm process, packing 54,200 million transistors into an 826 mm² die. The A10G uses the GA102 chip on Samsung's 8 nm node, with 28,300 million transistors on a 628 mm² die. This results in a transistor density of 65.6M per mm² for the A100 versus 45.1M per mm² for the A10G, reflecting the A100's more advanced manufacturing process.

The core configurations differ substantially. The A100 has 6,912 shading units, 432 texture mapping units, 160 ROPs, and 432 tensor cores, but no dedicated RT cores listed. The A10G has 9,216 shading units, 288 TMUs, 96 ROPs, 72 RT cores, and 288 tensor cores. This means the A10G has 33.3% more shading units and full ray tracing hardware, while the A100 has 50% more TMUs, 66.7% more ROPs, and 50% more tensor cores.

Memory architecture is the most striking difference. The A100 employs HBM2e with a 5120-bit bus and 2.04 TB/s bandwidth, whereas the A10G uses GDDR6 with a 384-bit bus and 600.2 GB/s bandwidth. Clock speeds also diverge: the A100 runs at 1275 MHz base and 1410 MHz boost, while the A10G runs at 1320 MHz base and 1710 MHz boost. The A10G's higher boost clock and greater shading unit count explain its FP32 advantage.

Physical and power characteristics further separate them. The A100 is an OAM module with no power connectors and no display outputs, requiring a 400 W TDP. The A10G is a single-slot card measuring 267 mm in length and 112 mm in height, using an 8-pin EPS connector, with a 150 W TDP. Both use a PCIe 4.0 x16 interface and have no display outputs.

The Verdict

The data strongly favors the A100 SXM4 80 GB for any workload that prioritizes memory bandwidth and tensor core density. Its 2.04 TB/s bandwidth is a category-leading feature, and the 26% Vulkan benchmark win over the A10G confirms its compute superiority in that specific test. The A100 also holds a percentile ranking of 98 versus 97, indicating it sits at the very top of the GPU performance distribution.

However, the A10G is not without merit. Its 31.52 TFLOPS FP32 performance is 61.7% higher than the A100's, making it the better choice for single-precision compute tasks that do not require massive memory bandwidth. The A10G's 150 W TDP and single-slot form factor also make it far more deployable in dense servers or systems with limited power and space, compared to the A100's 400 W OAM module.

For organizations running large language models, scientific simulations, or data-intensive AI training, the A100's HBM2e bandwidth and 80 GB capacity are likely to be decisive. For inference workloads, virtualized desktops, or any scenario where power efficiency and physical footprint matter more than raw bandwidth, the A10G offers a compelling alternative. The choice is not about which is "better" overall, but which is better suited to the specific constraint of the deployment environment.

Specification Differences

| Specification | NVIDIA A100 SXM4 80 GB | NVIDIA A10G |

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

| Process Node | 7 nm (TSMC) | 8 nm (Samsung) |

| Transistors | 54,200 million | 28,300 million |

| Die Size | 826 mm² | 628 mm² |

| Transistor Density | 65.6M / mm² | 45.1M / mm² |

| Base Clock | 1275 MHz | 1320 MHz |

| Boost Clock | 1410 MHz | 1710 MHz |

| Memory Size | 80 GB | 24 GB |

| Memory Type | HBM2e | GDDR6 |

| Memory Bus | 5120 bit | 384 bit |

| Memory Bandwidth | 2.04 TB/s | 600.2 GB/s |

| Shading Units | 6912 | 9216 |

| TMUs | 432 | 288 |

| ROPs | 160 | 96 |

| RT Cores | Not listed | 72 |

| Tensor Cores | 432 | 288 |

| FP32 Performance | 19.49 TFLOPS | 31.52 TFLOPS |

| FP16 Performance | 77.97 TFLOPS (4:1) | 31.52 TFLOPS (1:1) |

| TDP | 400 W | 150 W |

| Slot Width | OAM Module | Single-slot |

| Power Connectors | None | 8-pin EPS |

| Suggested PSU | 800 W | 450 W |

| DirectX Support | Not listed | 12 Ultimate (12_2) |

| OpenGL Support | Not listed | 4.6 |

| Vulkan Support | Not listed | 1.4 |

| Dimensions | Not listed | 267 mm x 112 mm |

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench Vulkan test, and the result is unambiguous. The A100 SXM4 80 GB scores 183,725, while the A10G scores 145,863. This yields a 26% delta in favor of the A100, a substantial margin that speaks to the A100's superior memory bandwidth and compute architecture in graphics-related compute tasks.

Contextualizing this score against rivals clarifies the magnitude. The A100's 183,725 score places it just 0.5% behind the NVIDIA RTX 5000 Ada Generation (184,664) and 0.9% ahead of the RTX PRO 5000 Blackwell (182,109). It also outperforms the GeForce RTX 4090 D (178,050) by 3.2%. The A100's position is firmly at the top tier of GPU performance.

The A10G's 145,863 Vulkan score is 1.1% ahead of the NVIDIA Tesla V100 PCIe 32 GB (150,305), but 5.4% behind the AMD Radeon Pro W6800X (160,671) and 6.5% behind the NVIDIA A100 PCIe 40 GB (162,504). Interestingly, the A10G's average benchmark score of 151,963 is actually lower than its Vulkan score, owing to its additional OpenCL result of 158,063. This indicates that the A10G's performance varies meaningfully across different compute APIs.

The A100's single benchmark score of 183,725 is identical to its average score, showing consistency in Vulkan performance. The 26% lead over the A10G in this test is the largest delta between the two cards in any available metric, highlighting the A100's dominance in this specific workload.

Where Each One Wins

NVIDIA A100 SXM4 80 GB wins in: memory-bandwidth-bound workloads. The 2.04 TB/s bandwidth versus 600.2 GB/s is a 3.4x advantage that directly benefits large matrix operations, transformer model training, and HPC simulations that repeatedly move data between memory and compute units. The 80 GB capacity also allows for larger batch sizes and bigger models than the A10G's 24 GB. The 432 tensor cores (50% more than the A10G) and FP16 throughput of 77.97 TFLOPS (versus 31.52 TFLOPS) make it the clear choice for AI training and mixed-precision workloads. Its 98th percentile ranking and 26% Vulkan win confirm it as the higher-performing card in the available benchmark.

NVIDIA A10G wins in: raw FP32 compute and deployment flexibility. With 31.52 TFLOPS versus 19.49 TFLOPS, the A10G is 61.7% faster in single-precision operations, making it better suited for traditional HPC codes that rely on FP32 without needing tensor cores. The 150 W TDP is 62.5% lower than the A100's 400 W, enabling denser server configurations and lower cooling requirements. The single-slot form factor (267 mm x 112 mm) and standard 8-pin EPS connector make it far easier to install in existing PCIe slots than the A100's OAM module. The 72 RT cores and full DirectX 12 Ultimate support also give it graphics capabilities that the A100 lacks, making it more versatile for visualization or ray tracing tasks. For inference serving where power is limited, the A10G's lower power draw per unit of FP32 performance is a practical advantage.