AMD Instinct MI100 vs NVIDIA A10G Comparison

The NVIDIA A10G and AMD Instinct MI100 are both end-of-life server accelerators, yet they represent two fundamentally different design philosophies aimed at compute acceleration. The data shows a clear, if narrow, overall winner in the A10G, but the MI100 offers distinct advantages in specific technical areas. The A10G achieves an average benchmark score of 151,963, placing it in the 97th percentile of all GPUs, while the MI100 scores 139,035, placing it in the 96th percentile. The head-to-head comparison is limited to a single Geekbench OpenCL test, which the A10G wins decisively with a score of 158,063 against the MI100’s 139,035, a difference of 13.7%. This benchmark data, combined with the architectural specifications, provides a complete picture of where each card excels.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, and the results are unambiguous. The NVIDIA A10G scores 158,063, which is 19,028 points higher than the AMD Instinct MI100’s 139,035. This translates to a 13.7% performance advantage for the A10G in this specific compute workload. This is a substantial lead, demonstrating that in general-purpose OpenCL compute, the A10G’s architecture provides a significant edge. In terms of the wider competitive landscape, the A10G’s average score of 151,963 is 9.3% higher than the MI100’s average score, a figure that aligns closely with the single-test delta. The A10G also sits just 1.1% above the NVIDIA Tesla V100 PCIe 32 GB, showing it is a direct successor in performance, while the MI100’s score of 139,035 is only 0.7% ahead of the NVIDIA Tesla V100 PCIe 16 GB. This places the MI100 in a performance tier with the older V100 series, while the A10G is clearly a step above, edging closer to the higher-tier A100, which it trails by 6.5%.

The margin of victory for the A10G is not trivial. A 13.7% lead in a compute benchmark like OpenCL can translate to significantly reduced processing times for large datasets. The data does not include any benchmark where the MI100 wins, as it records zero wins in the head-to-head comparison. While the MI100 has a higher theoretical texture fill rate of 721.0 GTexel/s compared to the A10G’s 492.5 GTexel/s, this does not manifest as a win in the available OpenCL test. The A10G also has a substantial lead in pixel rate, with 164.2 GPixel/s versus the MI100’s 96.13 GPixel/s, which is a 70% advantage. This indicates that the A10G’s compute and rendering pipelines are more efficient in this benchmark, despite the MI100’s higher raw texture processing capability. The overall average benchmark scores further underscore this, with the A10G’s 151,963 average being 12,928 points higher than the MI100’s 139,035 average.

The Verdict

From the data, the NVIDIA A10G is the superior choice for general-purpose compute workloads that are represented by the Geekbench OpenCL benchmark. It is faster by a meaningful 13.7% and holds a higher percentile rank among all GPUs (97th vs 96th). For users running applications that rely on standard compute APIs like OpenCL, the A10G is the clear performance pick. Its architecture delivers more raw performance in this test, and its average benchmark score confirms that this is not a one-off result. The A10G is the better card for tasks where raw compute throughput and compatibility with APIs like DirectX 12 Ultimate and Vulkan are important.

However, the AMD Instinct MI100 is not without its merits. The data shows it offers double the memory capacity (32 GB vs 24 GB) and over double the memory bandwidth (1.23 TB/s vs 600.2 GB/s). This makes the MI100 a compelling option for workloads that are heavily memory-bound, where the capacity and speed of the VRAM are more critical than the raw compute throughput measured by OpenCL. The MI100 also has a higher transistor count (25,600 million vs 28,300 million) on a more advanced 7 nm process, but this does not translate to a benchmark win. The choice between the two comes down to whether the user prioritizes peak compute performance (A10G) or memory capacity and bandwidth (MI100). For a general compute workload, the A10G is the safer, more performant choice.

Where Each One Wins

The NVIDIA A10G wins in scenarios requiring maximum floating-point performance and modern API support. Its FP32 throughput is 31.52 TFLOPS, which is significantly higher than the MI100’s 23.07 TFLOPS, a 36.6% advantage. This makes the A10G better suited for single-precision compute tasks like AI inference, graphics rendering, and scientific simulations that rely heavily on FP32 operations. The A10G also supports a full suite of modern graphics APIs, including DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, while the MI100 lists N/A for all of these. This makes the A10G the only viable option for any workload that requires a standard graphics or compute API other than OpenCL. Its higher pixel rate (164.2 GPixel/s) and inclusion of ray tracing cores (72) and tensor cores (288) further solidify its lead in graphics and AI-accelerated tasks.

The AMD Instinct MI100 wins in the domain of memory-intensive applications. Its 32 GB of HBM2 memory, compared to the A10G’s 24 GB of GDDR6, provides a 33% capacity increase, allowing larger datasets to be loaded into VRAM without spilling to system memory. More importantly, its memory bandwidth of 1.23 TB/s is more than double the A10G’s 600.2 GB/s. This is a massive advantage for workloads like large language model training, big data analytics, or high-resolution scientific visualization, where data movement is the primary bottleneck. The MI100 also offers higher FP16 throughput at 46.14 TFLOPS (2:1) compared to the A10G’s 31.52 TFLOPS (1:1). This makes the MI100 a potentially better option for workloads that can utilize mixed-precision arithmetic, such as certain machine learning training loops, despite its lower overall FP32 performance. Its higher texture rate (721.0 GTexel/s) also suggests it could excel in tasks that involve heavy texture filtering, though this does not translate to a win in the available OpenCL benchmark.

FAQ

Q: Based on the benchmark data, which GPU is faster?

A: The NVIDIA A10G is faster. In the Geekbench OpenCL test, it scored 158,063 compared to the AMD Instinct MI100’s 139,035, which is a 13.7% performance advantage for the A10G.

Q: Does the AMD Instinct MI100 have any performance advantages over the NVIDIA A10G?

A: Yes, in terms of memory. The MI100 has a higher memory bandwidth of 1.23 TB/s compared to the A10G’s 600.2 GB/s, and it also has more memory capacity at 32 GB versus 24 GB.

Q: Which GPU is better for high-performance computing (HPC) workloads?

A: The data suggests the NVIDIA A10G has a higher FP32 compute rate at 31.52 TFLOPS versus the MI100’s 23.07 TFLOPS. However, the MI100 offers a higher FP16 rate at 46.14 TFLOPS (2:1) and significantly more memory bandwidth, which could be better for specific memory-bound HPC tasks.

Q: Can either of these GPUs be used for gaming or traditional graphics?

A: The NVIDIA A10G is the only one with standard graphics API support, listing DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The AMD Instinct MI100 lists N/A for all graphics APIs, and both cards have no display outputs.

Q: How does the NVIDIA A10G compare to the NVIDIA A100 PCIe 40 GB?

A: Based on the nearest rivals data, the A10G has an average benchmark score of 151,963, which is 6.5% lower than the A100 PCIe 40 GB’s average score of 162,504.

Q: What is the difference in their manufacturing processes?

A: The AMD Instinct MI100 is built on a 7 nm process at TSMC, while the NVIDIA A10G is built on an 8 nm process at Samsung.

Architecture Differences

The NVIDIA A10G and AMD Instinct MI100 are built on fundamentally different architectures, which explains their distinct performance profiles. The A10G uses the NVIDIA Ampere architecture (chip GA102), fabricated on an 8 nm process at Samsung. It contains 28,300 million transistors on a die size of 628 mm², resulting in a transistor density of 45.1 million transistors per mm². The MI100, in contrast, uses the AMD CDNA 1.0 architecture (chip Arcturus), fabricated on a more advanced 7 nm process at TSMC. It packs 25,600 million transistors on a larger die size of 750 mm², leading to a lower transistor density of 34.1 million transistors per mm². This indicates that the A10G has a denser, more efficient packing of transistors.

The memory systems are drastically different. The A10G features 24 GB of GDDR6 memory on a 384-bit bus, providing a bandwidth of 600.2 GB/s. The MI100 features 32 GB of HBM2 memory on a massive 4096-bit bus, delivering a bandwidth of 1.23 TB/s. This architectural choice is the primary reason for the MI100’s memory advantage. The compute units also differ significantly. The A10G has 9,216 shading units, 288 texture mapping units (TMUs), and 96 render output units (ROPs). It also includes 72 ray tracing cores and 288 tensor cores. The MI100 has 7,680 shading units, 480 TMUs, and 64 ROPs, but notably, it has no ray tracing or tensor cores listed. This shows the A10G is designed to be a more versatile accelerator, capable of handling graphics and AI-specific tasks, while the MI100 is a pure compute processor.

Clock speeds and power consumption also show contrasting priorities. The A10G has a base clock of 1320 MHz and a boost clock of 1710 MHz, with a TDP of 150 W and a single-slot design. The MI100 has a lower base clock of 1000 MHz and a boost clock of 1502 MHz, but consumes double the power with a 300 W TDP and requires a dual-slot design. This makes the A10G significantly more power-efficient for the performance it delivers. The A10G’s support for a wider range of APIs, including DirectX 12 Ultimate, OpenGL, and Vulkan, is a direct result of its Ampere architecture, which is a more general-purpose design compared to the compute-focused CDNA 1.0. The MI100’s lack of API support beyond the compute-oriented OpenCL highlights its singular focus on raw computational throughput, rather than general-purpose graphics or compute tasks.