AMD Instinct MI100 vs NVIDIA A100 SXM4 80 GB Comparison

The NVIDIA A100 SXM4 80 GB and the AMD Instinct MI100 represent two distinct approaches to server-class acceleration from the same generation. Both are built on TSMC's 7 nm process and launched on the same date, yet the data reveals they occupy different performance tiers. The A100 SXM4 80 GB achieves a Geekbench Vulkan score of 183,725, placing it in the 98th percentile of all GPUs, while the MI100 delivers a Geekbench OpenCL score of 139,035, sitting in the 96th percentile. This places the A100 roughly 32% ahead of the MI100 in their respective benchmark tests, a gap that underscores significant architectural and specification differences between the two accelerators.

Head-to-Head Benchmarks

The benchmark results show a clear performance hierarchy favoring the NVIDIA A100 SXM4 80 GB. In its Geekbench Vulkan test, the A100 scores 183,725 points, which is 44,690 points higher than the MI100's OpenCL score of 139,035 — a 32.1% advantage. This is a substantial margin in raw compute throughput, and it aligns with the A100's higher peak specifications in several key areas. The A100's FP32 throughput is 19.49 TFLOPS, while the MI100 reaches 23.07 TFLOPS, meaning the AMD part actually leads in standard single-precision compute. However, the A100's FP16 performance of 77.97 TFLOPS (4:1) dwarfs the MI100's 46.14 TFLOPS (2:1), suggesting the NVIDIA part is far better optimized for mixed-precision workloads common in AI training.

Looking at the A100's nearest rivals provides context for its score. The A100 SXM4 80 GB sits just 0.5% behind the NVIDIA RTX 5000 Ada Generation (184,664) and 0.9% ahead of the NVIDIA RTX PRO 5000 Blackwell (182,109). It trails the A100 SXM4 40 GB variant by 1.8% (187,147) but beats the GeForce RTX 4090 D by 3.2% (178,050). This positions the 80 GB A100 as a top-tier performer even among its own family, with the 40 GB version holding a slight edge in the Geekbench Vulkan test. The differences are small, indicating that the 80 GB memory configuration does not hurt compute performance relative to the 40 GB model.

For the MI100, the competitive landscape is different. Its OpenCL score of 139,035 places it just 0.7% ahead of the NVIDIA Tesla V100 PCIe 16 GB (138,063) and 0.9% ahead of the Tesla V100 SXM2 32 GB (137,731). It also leads the AMD Radeon PRO V620 by 1.9% (136,472) and the Radeon Pro W6800X Duo by 2.4% (135,774). These are modest margins, showing that the MI100's performance is closely matched with the previous-generation V100 parts, rather than competing with the newer Ampere-based A100. The data suggests the MI100 is a capable accelerator but not a class leader in the same way the A100 is.

The FP16 disparity is particularly telling. The A100's 77.97 TFLOPS FP16 output is 1.69 times higher than the MI100's 46.14 TFLOPS. This is a critical metric for deep learning workloads, where tensor operations dominate. While the MI100 has more shading units (7,680 vs. 6,912) and more TMUs (480 vs. 432), it lacks dedicated tensor cores entirely, whereas the A100 features 432 tensor cores. This architectural advantage allows the A100 to excel in AI inference and training tasks, which are increasingly the primary use case for server accelerators.

Memory bandwidth also favors the A100 significantly. The A100 SXM4 80 GB provides 2.04 TB/s of bandwidth from its HBM2e memory, compared to the MI100's 1.23 TB/s from HBM2. That is a 65.9% higher bandwidth figure, which directly impacts performance in memory-bound workloads such as large matrix operations and data-intensive inference. The A100 also offers 80 GB of memory versus the MI100's 32 GB, a 2.5x capacity advantage that allows larger models and datasets to reside on the GPU without spilling to system memory.

The Verdict

The data points to a clear winner for most server workloads: the NVIDIA A100 SXM4 80 GB. With a 32.1% higher benchmark score, 1.69x FP16 throughput, 2.5x memory capacity, and 65.9% more memory bandwidth, it dominates the MI100 in nearly every measurable compute category. The A100's 98th percentile ranking versus the MI100's 96th percentile confirms that the NVIDIA part sits in a higher performance class. For organizations focused on AI training, inference, or scientific computing that leverages FP16 and tensor operations, the A100 is the superior choice based on the benchmark data alone.

However, the MI100 is not without merit. It actually leads in raw FP32 performance (23.07 TFLOPS vs. 19.49 TFLOPS), a 18.4% advantage that could matter for traditional HPC simulation workloads that rely on single-precision math without tensor acceleration. It also has a lower TDP of 300 W versus the A100's 400 W, and its dual-slot form factor with 2x 8-pin power connectors makes it easier to integrate into standard servers compared to the A100's OAM module. For users with existing PCIe-based infrastructure who need pure FP32 capability and can accept less memory, the MI100 is a viable option — but the benchmark data suggests it is closer to the previous-generation Tesla V100 series than to the A100.

When choosing between these two, the decision hinges on workload characteristics. If the primary tasks are deep learning, AI inference, or any workload that benefits from tensor cores and high FP16 throughput, the A100 SXM4 80 GB is the clear pick. Its 77.97 TFLOPS FP16 performance and 432 tensor cores provide a massive advantage that the MI100 cannot match. Conversely, if the workload is exclusively FP32-heavy and memory capacity is less critical, the MI100's higher FP32 throughput and lower power draw make it a reasonable alternative — but users must accept a significantly lower overall benchmark score and a smaller memory footprint.

Architecture Differences

The two accelerators take fundamentally different architectural approaches. The NVIDIA A100 SXM4 80 GB is built on the Ampere architecture, using the GA100 chip, which packs 54,200 million transistors on an 826 mm² die. This results in a transistor density of 65.6 million transistors per mm². The MI100 uses AMD's CDNA 1.0 architecture with the Arcturus chip, incorporating 25,600 million transistors on a 750 mm² die, yielding a density of 34.1 million per mm². The A100's higher density reflects a more complex design with dedicated tensor cores and a wider memory interface.

The A100 features 432 tensor cores, while the MI100 has none. This is the most significant architectural divergence. Tensor cores are specialized hardware for matrix multiplication, which is the core operation in neural network training and inference. The MI100 relies on its 7,680 shading units (versus the A100's 6,912) to handle compute tasks, but without tensor acceleration, it cannot match the A100's FP16 throughput. The A100 also has 160 ROPs compared to the MI100's 64, giving it a pixel rate of 225.6 GPixel/s versus 96.13 GPixel/s — a 2.35x advantage, though this is less relevant for non-display server workloads.

Memory architecture differs as well. The A100 uses HBM2e memory with a 5,120-bit bus, while the MI100 uses HBM2 with a 4,096-bit bus. The A100's memory clock is 1,593 MHz (3.2 Gbps effective), versus the MI100's 1,200 MHz (2.4 Gbps effective). These differences combine to produce the A100's 2.04 TB/s bandwidth versus the MI100's 1.23 TB/s. The MI100's texture rate of 721.0 GTexel/s actually exceeds the A100's 609.1 GTexel/s, thanks to its 480 TMUs versus 432, but this does not translate to a benchmark win.

Specification Differences

The specification sheets reveal several key differences beyond the benchmark scores. The A100 SXM4 80 GB has a base clock of 1,275 MHz and a boost clock of 1,410 MHz, while the MI100 runs at 1,000 MHz base and 1,502 MHz boost. The MI100's higher boost clock partially explains its FP32 advantage despite having fewer functional units per clock. The A100's memory configuration is 80 GB of HBM2e, while the MI100 offers 32 GB of HBM2 — a difference in both capacity and memory generation.

Power and physical specifications diverge sharply. The A100 has a TDP of 400 W and uses an OAM Module slot width with no power connectors (power is supplied through the module interface). The MI100 has a 300 W TDP and is a dual-slot card requiring 2x 8-pin power connectors. The A100 suggests an 800 W power supply, while the MI100 suggests 700 W. The MI100 has defined physical dimensions of 267 mm in length and 111 mm in height, whereas the A100's dimensions are not specified. Both use a PCIe 4.0 x16 bus interface and have no display outputs.

Neither product has a launch MSRP listed, and both are marked as end-of-life. The A100's generation is "Server Ampere (Axx)" with a predecessor of "Tesla Turing" and a successor of "Server Ada." The MI100's generation is "Instinct (MIx)" with a predecessor of "Radeon Instinct" and no successor listed. Both were released on the same date and have no API support for DirectX, OpenGL, or Vulkan (the MI100 lists "N/A" for all three), confirming their compute-only positioning.

FAQ

Q: Which GPU has a higher benchmark score?

A: The NVIDIA A100 SXM4 80 GB scores 183,725 in Geekbench Vulkan, which is 32.1% higher than the AMD Instinct MI100's 139,035 in Geekbench OpenCL.

Q: Does the AMD Instinct MI100 have any performance advantage?

A: Yes, the MI100 has higher FP32 throughput at 23.07 TFLOPS versus the A100's 19.49 TFLOPS, and it also has a higher texture rate of 721.0 GTexel/s compared to 609.1 GTexel/s.

Q: How does memory capacity differ between the two?

A: The A100 SXM4 80 GB offers 80 GB of HBM2e memory, while the MI100 provides 32 GB of HBM2. The A100 also has higher memory bandwidth at 2.04 TB/s versus 1.23 TB/s.

Q: What is the power consumption difference?

A: The A100 has a TDP of 400 W and suggests an 800 W power supply, while the MI100 has a TDP of 300 W and suggests a 700 W power supply.

Q: Are these GPUs still in production?

A: No, both are marked as end-of-life. The A100 has a successor listed as "Server Ada," while the MI100 has no successor listed.

Q: Which GPU is better for deep learning workloads?

A: Based on the data, the A100 is better for deep learning due to its 432 tensor cores and 77.97 TFLOPS FP16 performance, compared to the MI100's lack of tensor cores and 46.14 TFLOPS FP16 output.