AMD Instinct MI100 vs NVIDIA A100 SXM4 40 GB Comparison

The NVIDIA A100 SXM4 40 GB and AMD Instinct MI100 are both end-of-life server accelerators built for high-performance compute, yet they occupy different performance tiers. The data shows a clear overall winner in the A100, which posts an average benchmark score of 187,147 compared to the MI100’s 139,035, placing the NVIDIA part in the 98th percentile of all GPUs versus the AMD part’s 96th. However, the AMD card offers a distinct set of architectural trade-offs that matter for specific workloads, making the choice between them less about raw speed and more about the nature of the compute task.

Where Each One Wins

The NVIDIA A100 SXM4 40 GB is the outright winner in every benchmark category recorded. In the sole head-to-head test, Geekbench OpenCL, the A100 scores 201,096 against the MI100’s 139,035, a decisive 44.6% advantage. This performance gap is consistent with the A100’s positioning: it sits 1.3% above the NVIDIA RTX 5000 Ada Generation and 1.9% ahead of the NVIDIA A100 SXM4 80 GB, while outpacing the older Tesla V100S PCIe 32 GB by 3.7% in average score. For any general-purpose compute workload measured by OpenCL, the A100 is the superior accelerator.

The AMD Instinct MI100, by contrast, does not win any recorded benchmark. Its advantage lies not in raw score but in efficiency and form factor. With a 300 W TDP versus the A100’s 400 W, the MI100 delivers its compute with 25% less power draw. It also comes as a dual-slot PCIe card with two 8-pin power connectors, making it more adaptable to standard server chassis, whereas the A100 is an SXM module with no power connectors, requiring a proprietary baseboard. The MI100’s nearest rival, the NVIDIA Tesla V100 PCIe 16 GB, scores only 0.7% lower, indicating the AMD part is competitive with that older NVIDIA generation but not with the Ampere-based A100.

In practical terms, the A100 wins where maximum throughput per accelerator is the priority, especially in dense HPC clusters or AI training nodes where its higher FP32 and FP16 rates (19.49 TFLOPS and 77.97 TFLOPS, respectively) can be fully utilized. The MI100 wins where power envelope and physical installation flexibility are constraints, such as retrofitting into existing PCIe servers without changing the power delivery infrastructure.

Architecture Differences

The architectural divide between these two accelerators is fundamental. The A100 uses NVIDIA’s GA100 chip on the Ampere architecture, fabricated on a 7 nm process at TSMC with 54,200 million transistors on a 826 mm² die, yielding a transistor density of 65.6 million per mm². The MI100 uses AMD’s Arcturus chip on the CDNA 1.0 architecture, also 7 nm TSMC, but with only 25,600 million transistors on a 750 mm² die, for a density of 34.1 million per mm². The A100 packs more than twice the transistors into a slightly larger die, which explains its higher compute throughput.

Memory subsystems diverge sharply. The A100 carries 40 GB of HBM2e across a 5120-bit bus, achieving 1.56 TB/s bandwidth. The MI100 has 32 GB of HBM2 on a 4096-bit bus, delivering 1.23 TB/s. The A100’s 25% larger memory pool and 27% higher bandwidth give it a clear edge for large models or datasets that exceed the MI100’s capacity. Clock speeds are closer: the A100 runs at 1095 MHz base and 1410 MHz boost, while the MI100 runs at 1000 MHz base but boosts higher to 1502 MHz.

Compute unit counts tell a nuanced story. The MI100 has more shading units (7,680 vs. 6,912) and more texture mapping units (480 vs. 432), leading to a higher texture rate of 721.0 GTexel/s versus the A100’s 609.1 GTexel/s. However, the A100 has far more ROPs (160 vs. 64) and a much higher pixel rate of 225.6 GPixel/s versus 96.13 GPixel/s. Critically, the A100 features 432 tensor cores, which are absent from the MI100 entirely. This makes the A100 uniquely suited for matrix math in AI inference and training, while the MI100 relies on its raw FP32 and FP16 vector throughput.

The FP16 ratio is another differentiator. The A100 achieves 77.97 TFLOPS FP16 at a 4:1 ratio relative to FP32, while the MI100 achieves 46.14 TFLOPS at a 2:1 ratio. This means the A100 can accelerate FP16 workloads far beyond its FP32 baseline, a key feature for deep learning, whereas the MI100’s FP16 gain is more modest. Both cards have no display outputs and no support for DirectX, OpenGL, or Vulkan, confirming their compute-only purpose.

FAQ

Q: Which accelerator has a higher average benchmark score?

A: The NVIDIA A100 SXM4 40 GB scores 187,147 on average, while the AMD Instinct MI100 scores 139,035, a difference of roughly 34.6% in favor of NVIDIA.

Q: How much faster is the A100 in the only shared benchmark test?

A: In Geekbench OpenCL, the A100 scores 201,096 versus the MI100’s 139,035, a 44.6% delta that represents the A100’s clear dominance in that workload.

Q: Does the MI100 have any advantage in power consumption?

A: Yes, the MI100 has a 300 W TDP compared to the A100’s 400 W, meaning it draws 25% less power while still delivering substantial FP32 compute of 23.07 TFLOPS.

Q: Why does the A100 have a higher memory bandwidth?

A: The A100 uses 40 GB of HBM2e on a 5120-bit bus for 1.56 TB/s, whereas the MI100 uses 32 GB of HBM2 on a 4096-bit bus for 1.23 TB/s, giving the A100 a 27% bandwidth advantage.

Q: Are these cards suitable for AI workloads?

A: The A100 is better suited due to its 432 tensor cores and high FP16 throughput of 77.97 TFLOPS; the MI100 has no tensor cores and delivers only 46.14 TFLOPS FP16, making it more of a general-purpose compute card.

Q: What are the physical differences in mounting?

A: The A100 is an SXM module with no power connectors, requiring a specialized server board, while the MI100 is a dual-slot PCIe card with two 8-pin connectors and dimensions of 267 mm in length, fitting standard server slots.

Specification Differences

| Specification | NVIDIA A100 SXM4 40 GB | AMD Instinct MI100 |

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

| Chip | GA100 | Arcturus |

| Architecture | Ampere | CDNA 1.0 |

| Transistors | 54,200 million | 25,600 million |

| Die Size | 826 mm² | 750 mm² |

| Transistor Density | 65.6M / mm² | 34.1M / mm² |

| Base Clock | 1095 MHz | 1000 MHz |

| Boost Clock | 1410 MHz | 1502 MHz |

| Memory Size | 40 GB | 32 GB |

| Memory Type | HBM2e | HBM2 |

| Memory Bus | 5120 bit | 4096 bit |

| Memory Bandwidth | 1.56 TB/s | 1.23 TB/s |

| Shading Units | 6912 | 7680 |

| TMUs | 432 | 480 |

| ROPs | 160 | 64 |

| Tensor Cores | 432 | None |

| Pixel Rate | 225.6 GPixel/s | 96.13 GPixel/s |

| Texture Rate | 609.1 GTexel/s | 721.0 GTexel/s |

| FP32 | 19.49 TFLOPS | 23.07 TFLOPS |

| FP16 | 77.97 TFLOPS (4:1) | 46.14 TFLOPS (2:1) |

| TDP | 400 W | 300 W |

| Slot Width | SXM Module | Dual-slot |

| Power Connectors | None | 2x 8-pin |

| Suggested PSU | 800 W | 700 W |

| Dimensions | N/A | 267 mm length, 111 mm height |

| Release Date | 2020-05-13 | 2020-11-15 |

Head-to-Head Benchmarks

The only direct benchmark comparison available is Geekbench OpenCL, and it is lopsided. The NVIDIA A100 SXM4 40 GB posts a score of 201,096, while the AMD Instinct MI100 manages 139,035. That 44.6% delta is the single largest performance gap in this analysis, and it aligns with the A100’s positioning against its own rivals: it outperforms the RTX 5000 Ada Generation by 1.3%, the A100 SXM4 80 GB by 1.9%, and the RTX PRO 5000 Blackwell by 2.8% in average score. The MI100, meanwhile, sits just 0.7% above the Tesla V100 PCIe 16 GB and 0.9% above the Tesla V100 SXM2 32 GB, indicating it competes with the previous generation of NVIDIA data center parts rather than the current one.

Drilling into the architectural reasons for this gap, the A100’s tensor cores are the primary differentiator. With 432 tensor cores and an FP16 throughput of 77.97 TFLOPS, the A100 is built for the 4:1 ratio that accelerates matrix operations far beyond its FP32 baseline. The MI100’s FP16 rate of 46.14 TFLOPS at a 2:1 ratio is impressive for a 300 W card, but it lacks the specialized hardware to match the A100’s AI-specific performance. In pure FP32, the MI100 actually wins on paper—23.07 TFLOPS versus 19.49 TFLOPS—but this does not translate to a benchmark victory, likely because OpenCL workloads often utilize mixed precision or memory-bound patterns where the A100’s superior bandwidth (1.56 TB/s vs. 1.23 TB/s) and larger memory pool (40 GB vs. 32 GB) take effect.

The pixel rate difference is stark: the A100 delivers 225.6 GPixel/s versus the MI100’s 96.13 GPixel/s, a 2.3x advantage that stems from the A100’s 160 ROPs versus 64. This matters less for compute but indicates the A100 has a more balanced rendering pipeline if ever used for visualization tasks. The MI100’s higher texture rate (721.0 GTexel/s vs. 609.1 GTexel/s) suggests it is better optimized for texture-heavy operations, but that is a niche advantage in a headless compute card. Ultimately, the data shows the A100 wins the only benchmark outright, and its architectural advantages in memory, tensor cores, and rasterization make it the more capable accelerator across the board.