AMD Instinct MI100 vs NVIDIA A100 PCIe 40 GB Comparison

# NVIDIA A100 PCIe 40 GB vs AMD Instinct MI100

The NVIDIA A100 PCIe 40 GB and AMD Instinct MI100 are both end-of-life server accelerators targeting high-performance compute workloads, yet benchmark data shows a decisive performance gap in favor of the A100. In the single available head-to-head comparison, the A100 delivers a Geekbench OpenCL score of 178,627 against the MI100's 139,035, a 28.5% advantage. Both cards occupy the top percentile tier among all GPUs — the A100 at the 97th percentile and the MI100 at the 96th — but their closest rivals reveal different competitive landscapes. The A100's average benchmark score of 162,504 places it 1.1% ahead of the AMD Radeon Pro W6800X and 1.4% behind the AMD Radeon PRO W7800, while the MI100's 139,035 average sits just 0.7% above the NVIDIA Tesla V100 PCIe 16 GB. These figures suggest the A100 competes at a higher absolute performance level, with architectural and specification differences explaining the gap.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA A100 PCIe 40 GB has an average benchmark score of 162,504, compared to the AMD Instinct MI100's 139,035 — a difference of 23,469 points in favor of the A100.

Q: How much faster is the A100 in the head-to-head OpenCL test?

A: In the Geekbench OpenCL benchmark, the A100 scores 178,627 versus the MI100's 139,035, giving the NVIDIA card a 28.5% performance advantage.

Q: What are the memory capacities and types of these two accelerators?

A: The A100 features 40 GB of HBM2e memory, while the MI100 has 32 GB of HBM2 memory. The A100 also has a wider memory bus at 5120-bit versus 4096-bit.

Q: Which GPU offers higher FP32 compute throughput?

A: The MI100 has a higher FP32 rating at 23.07 TFLOPS, compared to the A100's 19.49 TFLOPS. However, the A100 leads in FP16 with 77.97 TFLOPS (4:1) versus the MI100's 46.14 TFLOPS (2:1).

Q: How do the transistor counts differ between these chips?

A: The A100's GA100 chip contains 54,200 million transistors on an 826 mm² die, while the MI100's Arcturus chip has 25,600 million transistors on a 750 mm² die. Both use a 7 nm TSMC process.

Q: What is the power consumption difference?

A: The A100 has a TDP of 250 W, while the MI100 draws 300 W. The A100 also requires a lower suggested power supply at 600 W versus 700 W for the MI100.

Architecture Differences

The architectural divide between these two accelerators is fundamental. The A100 is built on NVIDIA's Ampere architecture with the GA100 chip, while the MI100 uses AMD's CDNA 1.0 architecture with the Arcturus chip. Both are fabricated on TSMC's 7 nm process, but the similarities end there. The A100 packs 54,200 million transistors into an 826 mm² die, achieving a transistor density of 65.6 million per mm². The MI100, by contrast, has 25,600 million transistors on a 750 mm² die, yielding a density of 34.1 million per mm² — roughly half the A100's density.

The A100 integrates 432 tensor cores, which the MI100 lacks entirely; AMD's architecture has no dedicated tensor core units. Instead, the MI100 relies on its 7,680 shading units, which outnumber the A100's 6,912 shading units. The MI100 also has more texture mapping units (480 versus 432) but fewer ROPs (64 versus 160). Clock speeds favor the MI100, with a base of 1000 MHz and boost of 1502 MHz, against the A100's 765 MHz base and 1410 MHz boost. The A100 compensates with higher memory bandwidth: 1.56 TB/s from HBM2e over a 5120-bit bus, versus 1.23 TB/s from HBM2 over a 4096-bit bus.

Feature-wise, the A100 supports a broader API profile with no listed null entries for DirectX, OpenGL, or Vulkan, while the MI100 explicitly lists N/A for all three. Both cards are dual-slot, have no display outputs, and use PCIe 4.0 x16 interfaces. The A100 requires a single 8-pin EPS power connector; the MI100 needs two 8-pin connectors. Release timing differs by five months: the A100 launched on 2020-06-21, and the MI100 followed on 2020-11-15.

Where Each One Wins

The A100 wins the only direct benchmark comparison, but each card has distinct strengths depending on workload characteristics. The A100's advantage lies in memory capacity and bandwidth. With 40 GB of HBM2e and 1.56 TB/s bandwidth, it can hold larger datasets and feed data to compute units faster than the MI100's 32 GB HBM2 at 1.23 TB/s. The A100 also doubles the MI100 in FP16 throughput — 77.97 TFLOPS versus 46.14 TFLOPS — making it the stronger choice for mixed-precision AI training and inference workloads that leverage tensor cores.

The MI100, however, holds a raw FP32 advantage. Its 23.07 TFLOPS exceeds the A100's 19.49 TFLOPS by roughly 18%, which benefits traditional HPC simulation and scientific computing where single-precision math dominates. The MI100 also has more shading units (7,680 versus 6,912) and a higher texture rate (721.0 GTexel/s versus 609.1 GTexel/s), suggesting better raw throughput in shader-heavy tasks. Its higher boost clock of 1502 MHz versus 1410 MHz further supports compute-bound scenarios.

Power efficiency favors the A100 despite its lower performance-per-watt ceiling in FP32. The A100's 250 W TDP versus the MI100's 300 W means the NVIDIA card delivers more bandwidth and memory per watt. In multi-GPU server deployments, the A100's lower power draw could allow denser configurations. The MI100's higher thermal envelope and dual 8-pin connectors indicate it demands more robust power delivery infrastructure.

Specification Differences

The two accelerators differ across nearly every major specification category. Memory configuration: the A100 has 40 GB HBM2e with a 5120-bit bus and 1.56 TB/s bandwidth; the MI100 has 32 GB HBM2 with a 4096-bit bus and 1.23 TB/s bandwidth. Compute units: the A100 has 6,912 shading units, 432 TMUs, 160 ROPs, and 432 tensor cores; the MI100 has 7,680 shading units, 480 TMUs, 64 ROPs, and no tensor cores. Clock speeds: the A100 runs at 765 MHz base and 1410 MHz boost; the MI100 at 1000 MHz base and 1502 MHz boost. Memory clock: both use 1215 MHz / 2.4 Gbps effective, though the A100's listing is formatted differently.

FP32 throughput: 19.49 TFLOPS for the A100 versus 23.07 TFLOPS for the MI100. FP16 throughput: 77.97 TFLOPS (4:1) for the A100 versus 46.14 TFLOPS (2:1) for the MI100. Pixel and texture rates: the A100 outputs 225.6 GPixel/s and 609.1 GTexel/s; the MI100 outputs 96.13 GPixel/s and 721.0 GTexel/s. Power: 250 W TDP with a single 8-pin EPS connector and 600 W suggested PSU for the A100; 300 W TDP with dual 8-pin connectors and 700 W suggested PSU for the MI100.

Chip characteristics: the A100's GA100 has 54,200 million transistors on an 826 mm² die (65.6M/mm²); the MI100's Arcturus has 25,600 million transistors on a 750 mm² die (34.1M/mm²). Both are dual-slot, 267 mm long, 111 mm tall, have no display outputs, and use PCIe 4.0 x16. The A100's predecessor is Tesla Turing and successor is Server Ada; the MI100's predecessor is Radeon Instinct with no successor listed. Release dates differ: 2020-06-21 for the A100, 2020-11-15 for the MI100.

Head-to-Head Benchmarks

The sole head-to-head benchmark is Geekbench OpenCL, and it is a decisive victory for the A100. The NVIDIA card scores 178,627 against the MI100's 139,035, a 28.5% delta. This gap is substantial and reflects the A100's architectural advantages in memory bandwidth, tensor core integration, and overall compute organization. The MI100's higher FP32 peak and shading unit count do not translate into OpenCL performance leadership, indicating that memory bandwidth and specialized compute paths matter more in this workload.

Contextualizing the scores through nearest rivals sharpens the picture. The A100's average score of 162,504 puts it 1.1% ahead of the AMD Radeon Pro W6800X (160,671) and 1.4% behind the AMD Radeon PRO W7800 (164,894). The MI100's average of 139,035 sits 0.7% above the NVIDIA Tesla V100 PCIe 16 GB (138,063) and 0.9% above the Tesla V100 SXM2 32 GB (137,731). In other words, the A100 operates in a performance tier roughly 17% higher than the MI100's neighborhood when comparing average scores — a margin consistent with the 28.5% head-to-head delta.

The MI100's closest rivals are older NVIDIA Tesla V100 variants, while the A100's rivals are newer AMD and NVIDIA workstation cards. This suggests the MI100 competes with a previous generation of accelerators, whereas the A100 holds its own against more recent offerings. The A100 also outperforms the MI100 in percentile ranking: 97th versus 96th among all GPUs, a narrow but meaningful distinction at the top end of the distribution.

Neither card shows a win for the MI100 in any benchmark category. The wins tally stands at 1 for the A100 and 0 for the MI100. While the MI100's FP32 advantage is theoretically significant, the available benchmark evidence does not capture a scenario where that translates into a win. Users prioritizing raw single-precision throughput might still prefer the MI100, but the data indicates the A100 delivers superior real-world performance in the measured workload, with additional benefits in memory capacity, bandwidth, and power efficiency.