NVIDIA A100 SXM4 40 GB vs NVIDIA A10M Comparison

The NVIDIA A100 SXM4 40 GB and the NVIDIA A10M are both server-class Ampere accelerators, but they occupy distinctly different positions in the lineup. The data shows a clear performance hierarchy, with the A100 SXM4 40 GB delivering a dominant lead in compute throughput, while the A10M counters with a far more accessible power profile and a different memory architecture. This analysis breaks down the benchmark results, architectural differences, and practical implications for each card.

Head-to-Head Benchmarks

The only shared benchmark in the data is Geekbench OpenCL, and the results are decisively in favor of the A100 SXM4 40 GB. It scores 201,096 points against the A10M's 135,230 points, a delta of 48.7%. This is not a marginal win; it is a substantial, almost half-again improvement in raw compute performance. The A100 SXM4 40 GB's average benchmark score of 187,147 further reinforces its standing, placing it in the 98th percentile of all GPUs. The A10M, with its average score of 135,230, sits in the 96th percentile, which is still strong but noticeably lower.

Looking at the nearest rivals provides context for these numbers. The A100 SXM4 40 GB's closest competitor is the NVIDIA RTX 5000 Ada Generation, which scores 184,664 (1.3% behind). It also edges out its own higher-capacity sibling, the A100 SXM4 80 GB, by 1.9%. This indicates that the 40 GB variant is already at the top of its performance class. The A10M, conversely, is virtually tied with the NVIDIA RTX 4000 Ada Generation (135,218, a 0% delta) and is only marginally ahead of AMD's Radeon PRO W6800 (135,396, a -0.1% delta). The data suggests that in this specific workload, the A10M is performing exactly at the level expected of its direct peers, whereas the A100 SXM4 40 GB is punching above its weight class.

The 48.7% delta in OpenCL is the single most important performance figure here. It tells a story of two different design intents: one is a compute monster for heavy lifting, the other is a more balanced, power-efficient workhorse. While the A10M has a higher boost clock (1635 MHz vs 1410 MHz) and more shading units (7168 vs 6912), it cannot overcome the A100's massive memory bandwidth and specialized compute resources in this test.

Where Each One Wins

The A100 SXM4 40 GB wins outright in pure compute throughput. Its 19.49 TFLOPS of FP32 performance and 77.97 TFLOPS of FP16 performance (4:1) are staggering numbers. The FP16 figure is particularly important; it is more than triple the A10M's FP16 output, which stands at 23.44 TFLOPS (1:1). If your workload involves mixed-precision training or inference, the A100 SXM4 40 GB is the clear choice. Its 432 tensor cores, compared to the A10M's 224, further solidify its advantage in AI and deep learning tasks. The 1.56 TB/s of memory bandwidth on the A100 is also a decisive factor for memory-bound problems, dwarfing the A10M's 500.2 GB/s.

The A10M wins in a different category: power efficiency and physical requirements. Its TDP is 150 W versus the A100's 400 W, and it requires a suggested power supply of only 450 W compared to 800 W. This is a massive difference from a system integration perspective. The A10M is a single-slot card with an 8-pin EPS connector, while the A100 SXM4 is an SXM module with no power connectors, meaning it needs a specialized baseboard. The A10M's PCIe 4.0 x16 interface allows it to be dropped into a standard server chassis, whereas the SXM form factor of the A100 demands a proprietary platform. In scenarios where space, power, and cooling are constrained, the A10M is the practical option.

The A10M also offers a higher boost clock (1635 MHz vs 1410 MHz), which can help in lightly threaded or latency-sensitive tasks that don't scale perfectly with raw core count. However, in the benchmark data, this clock advantage does not translate into a performance win. The A10M's 56 RT cores are a feature the A100 lacks entirely, making it the only choice here for ray-traced workloads, though the server focus of both cards makes this a niche consideration.

Architecture Differences

Both GPUs are built on the Ampere architecture, but they are fundamentally different chips. The A100 SXM4 40 GB uses the GA100 die, fabricated on a 7 nm process at TSMC. The A10M uses the GA102 die, fabricated on an 8 nm process at Samsung. This process difference is significant: the GA100 packs 54,200 million transistors into an 826 mm² die, yielding a transistor density of 65.6 million per mm². The GA102, by contrast, contains 28,300 million transistors on a 628 mm² die, with a lower density of 45.1 million per mm². The A100's chip is not just bigger; it is denser, which contributes to its higher compute throughput.

Memory is another major differentiator. The A100 SXM4 40 GB uses HBM2e with a 5120-bit bus, delivering 1.56 TB/s of bandwidth. The A10M uses GDDR6 with a 320-bit bus, delivering 500.2 GB/s. This is a three-fold difference in bandwidth, which is critical for large datasets and matrix operations. The A100 also has a higher memory clock (1215 MHz, 2.4 Gbps effective) compared to the A10M (1563 MHz, 12.5 Gbps effective), but the A10M's higher effective rate does not compensate for its narrower bus.

The compute unit counts tell a similar story. The A100 has 432 TMUs and 160 ROPs, while the A10M has 224 TMUs and 80 ROPs. The A100's texture rate is 609.1 GTexel/s versus the A10M's 366.2 GTexel/s, and its pixel rate is 225.6 GPixel/s versus 130.8 GPixel/s. The A10M does have more shading units (7168 vs 6912), but the A100's other resources more than compensate. The A100 also lacks RT cores, while the A10M has 56, a clear architectural divergence. The A10M supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the A100's API support is not specified in the data, suggesting it is not designed for graphics workloads.

The Verdict

The data paints a clear picture: the NVIDIA A100 SXM4 40 GB is the superior performer for compute-intensive tasks, and the NVIDIA A10M is the superior choice for deployment flexibility. If your primary concern is raw throughput in FP32, FP16, or memory-bandwidth-bound operations, the A100 SXM4 40 GB is the only rational choice. Its 48.7% lead in OpenCL is backed by a 3.3x advantage in FP16 performance and a 3.1x advantage in memory bandwidth. The 98th percentile ranking confirms it is a top-tier accelerator.

The A10M, however, is not a weak card. It sits in the 96th percentile and is perfectly competitive with its immediate rivals. Its 150 W TDP and single-slot form factor make it a practical drop-in solution for servers where the A100's SXM module cannot be accommodated. If your workload is not massively parallel or does not require enormous memory bandwidth, the A10M's higher boost clock and standard PCIe interface may offer a more straightforward path to deployment. The presence of RT cores and full graphics API support also makes it a more versatile option for mixed workloads. The verdict is simple: choose the A100 SXM4 40 GB for maximum compute, choose the A10M for practical, power-conscious integration.

FAQ

Q: Which card has a higher average benchmark score?

A: The NVIDIA A100 SXM4 40 GB has a significantly higher average benchmark score of 187,147, compared to the NVIDIA A10M's 135,230.

Q: How much faster is the A100 SXM4 40 GB in Geekbench OpenCL?

A: The A100 SXM4 40 GB scores 201,096, which is 48.7% higher than the A10M's 135,230.

Q: What is the difference in memory bandwidth between the two cards?

A: The A100 SXM4 40 GB offers 1.56 TB/s of bandwidth using HBM2e, while the A10M offers 500.2 GB/s using GDDR6, making the A100 roughly three times faster in this metric.

Q: Which card has a lower thermal design power (TDP)?

A: The NVIDIA A10M has a TDP of 150 W, which is significantly lower than the A100 SXM4 40 GB's 400 W.

Q: Does the A10M support ray tracing?

A: Yes, the A10M has 56 RT cores, while the A100 SXM4 40 GB has no RT cores specified.

Q: What is the form factor difference between the two cards?

A: The A100 SXM4 40 GB is an SXM module, while the A10M is a single-slot card with a standard PCIe 4.0 x16 interface.

Specification Differences

| Specification | NVIDIA A100 SXM4 40 GB | NVIDIA A10M |

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

| Process Node | 7 nm | 8 nm |

| Foundry | TSMC | Samsung |

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

| Die Size | 826 mm² | 628 mm² |

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

| Base Clock | 1095 MHz | 975 MHz |

| Boost Clock | 1410 MHz | 1635 MHz |

| Memory Clock | 1215 MHz (2.4 Gbps effective) | 1563 MHz (12.5 Gbps effective) |

| Memory Size | 40 GB | 20 GB |

| Memory Type | HBM2e | GDDR6 |

| Memory Bus Width | 5120 bit | 320 bit |

| Memory Bandwidth | 1.56 TB/s | 500.2 GB/s |

| Shading Units | 6912 | 7168 |

| TMUs | 432 | 224 |

| ROPs | 160 | 80 |

| RT Cores | None | 56 |

| Tensor Cores | 432 | 224 |

| Pixel Rate | 225.6 GPixel/s | 130.8 GPixel/s |

| Texture Rate | 609.1 GTexel/s | 366.2 GTexel/s |

| FP32 Performance | 19.49 TFLOPS | 23.44 TFLOPS |

| FP16 Performance | 77.97 TFLOPS (4:1) | 23.44 TFLOPS (1:1) |

| TDP | 400 W | 150 W |

| Slot Width | SXM Module | Single-slot |

| Power Connectors | None | 8-pin EPS |

| Suggested PSU | 800 W | 450 W |

| Dimensions | Not specified | 267 mm (10.5 inches) length, 112 mm (4.4 inches) height |

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

| OpenGL Support | Not specified | 4.6 |

| Vulkan Support | Not specified | 1.4 |

| Release Date | 2020-05-13 | Not specified |