AMD Instinct MI300X vs NVIDIA A100 PCIe 80 GB Comparison

The AMD Instinct MI300X and NVIDIA A100 PCIe 80 GB represent two distinct generations of data center acceleration. The data places the MI300X decisively ahead in raw compute, but the A100’s architectural legacy and efficiency metrics tell a different story. This analysis relies exclusively on the provided benchmark and specification data.

Head-to-Head Benchmarks

The sole head-to-head benchmark available is the Geekbench OpenCL test. In this test, the AMD Instinct MI300X scores 317,994 points, while the NVIDIA A100 PCIe 80 GB scores 207,124 points. The MI300X wins with a delta of 53.5%. This is a substantial performance gap, indicating that the AMD part delivers over half again as much compute throughput in this workload.

Contextualizing these scores against their respective nearest rivals clarifies the competitive landscape. The MI300X’s score of 317,994 places it 5% behind the NVIDIA H200 NVL (334,891) and 8% behind the NVIDIA B200 (345,482). However, it is 7.5% ahead of the NVIDIA L40S (295,763) and 10.7% ahead of the NVIDIA RTX 6000 Ada Generation (287,237). The MI300X sits at the 100th percentile of all GPUs, reflecting its position near the top of the performance hierarchy.

The A100 PCIe 80 GB, with its 207,124 score, is 5.8% behind the AMD Radeon PRO W7900D (219,827) and 8% behind the NVIDIA PG506-232 (225,124). It is 5.7% ahead of the NVIDIA RTX 6000D (195,964) and 6.5% ahead of the NVIDIA Tesla V100S PCIe 32 GB (194,415). The A100’s percentile rank is 99, placing it just below the absolute top tier but still among the elite. The 53.5% delta between the MI300X and the A100 is far larger than the differences observed among either card’s nearest rivals, highlighting a generational leap in peak compute capability.

Where Each One Wins

The benchmark data shows a single decisive win for the AMD Instinct MI300X in the Geekbench OpenCL test. This indicates that for applications heavily reliant on raw, parallel floating-point throughput, the MI300X is the superior choice. The MI300X also wins decisively on memory capacity and bandwidth, which are critical for large language model inference and training.

The NVIDIA A100 PCIe 80 GB, while losing the compute benchmark, has distinct advantages in other areas. Its thermal design power is 300 W, compared to the MI300X’s 750 W. This efficiency differential is not a performance score, but it is a critical operational factor. The A100 is a dual-slot PCIe card with a standard 8-pin EPS connector, while the MI300X is an OAM module with no power connectors, requiring a different system infrastructure. The A100 is also smaller, measuring 267 mm in length, while the MI300X’s dimensions are not specified.

The A100’s architecture includes 432 tensor cores, a feature not present in the MI300X’s spec sheet. Furthermore, the A100 has a pixel rate of 225.6 GPixel/s, while the MI300X has a pixel rate of 0 MPixel/s. This suggests the MI300X is not designed for traditional graphics rasterization tasks, while the A100 retains some of those capabilities. The A100 also supports a 4:1 FP16 ratio (77.97 TFLOPS) versus its FP32 rate (19.49 TFLOPS), whereas the MI300X offers a 1:1 FP16 ratio (81.72 TFLOPS for both FP16 and FP32). This means the MI300X does not rely on a reduced-precision boost to achieve its high throughput.

The Verdict

Based strictly on the data, the AMD Instinct MI300X is the clear winner for raw compute performance. Its 53.5% lead in Geekbench OpenCL and its position at the 100th percentile make it the more powerful accelerator. For users whose primary concern is maximum throughput in parallel workloads, the MI300X is the data-driven choice.

The NVIDIA A100 PCIe 80 GB is the appropriate pick for scenarios prioritizing power efficiency and system integration ease. Its 300 W TDP is 450 W lower than the MI300X. Its PCIe 4.0 x16 interface and dual-slot form factor allow for deployment in standard server chassis, whereas the MI300X’s OAM module requires a specialized baseboard. The A100 is an end-of-life product, but its 99th percentile performance remains respectable.

The data does not support a scenario where the A100 wins on compute performance. The MI300X is faster, has 112 GB more memory (192 GB vs 80 GB), and offers over 2.7 times the memory bandwidth (5.32 TB/s vs 1.94 TB/s). The selection should be driven by infrastructure constraints and power budgets, where the A100 has a clear advantage.

FAQ

Q: Which GPU has the higher Geekbench OpenCL score?

A: The AMD Instinct MI300X scores 317,994, which is 53.5% higher than the NVIDIA A100 PCIe 80 GB's score of 207,124.

Q: How does the MI300X compare to the NVIDIA H200 NVL?

A: The MI300X’s average benchmark score of 317,994 is 5% lower than the NVIDIA H200 NVL’s average score of 334,891.

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

A: The AMD Instinct MI300X has a memory bandwidth of 5.32 TB/s, while the NVIDIA A100 PCIe 80 GB has a memory bandwidth of 1.94 TB/s.

Q: What is the thermal design power (TDP) for each card?

A: The AMD Instinct MI300X has a TDP of 750 W, and the NVIDIA A100 PCIe 80 GB has a TDP of 300 W.

Q: What is the production status of the NVIDIA A100 PCIe 80 GB?

A: The production status is listed as "End-of-life."

Q: Does the AMD Instinct MI300X have tensor cores?

A: The data does not list tensor cores for the MI300X. The NVIDIA A100 PCIe 80 GB has 432 tensor cores.

Architecture Differences

The two accelerators are built on fundamentally different architectures and process nodes. The AMD Instinct MI300X uses the CDNA 3.0 architecture with the "Aqua Vanjaram" chip, fabricated on a 5 nm process by TSMC. The NVIDIA A100 PCIe 80 GB uses the Ampere architecture with the GA100 chip, fabricated on a 7 nm process, also by TSMC.

The transistor counts differ dramatically. The MI300X integrates 153,000 million transistors on a 1017 mm² die, resulting in a density of 150.4M transistors per mm². The A100 has 54,200 million transistors on an 826 mm² die, for a density of 65.6M transistors per mm². The MI300X’s higher density reflects its more advanced process node.

Memory configurations are starkly different. The MI300X features 192 GB of HBM3 memory on an 8192-bit bus, while the A100 features 80 GB of HBM2e memory on a 5120-bit bus. This contributes to the MI300X’s significantly higher bandwidth.

The compute resources also differ. The MI300X has 19,456 shading units and 1,216 TMUs, but no ROPs and a pixel rate of 0 MPixel/s. The A100 has 6,912 shading units, 432 TMUs, and 160 ROPs, with a pixel rate of 225.6 GPixel/s. The MI300X’s FP32 throughput is 81.72 TFLOPS, while its FP16 is also 81.72 TFLOPS (1:1). The A100’s FP32 is 19.49 TFLOPS, with FP16 at 77.97 TFLOPS (4:1). The A100 also has 432 tensor cores, a feature absent from the MI300X’s data.

Clock speeds show the MI300X has a lower base clock (1000 MHz) but a higher boost clock (2100 MHz) compared to the A100’s 1065 MHz base and 1410 MHz boost. The system interfaces differ as well: the MI300X uses PCIe 5.0 x16, while the A100 uses PCIe 4.0 x16. The MI300X is an OAM module with no power connectors, while the A100 is a dual-slot card with an 8-pin EPS connector. The suggested PSU is 1150 W for the MI300X and 700 W for the A100. The release dates are December 5, 2023, for the MI300X and June 27, 2021, for the A100.