GPU Comparison

Head-to-Head Benchmarks

The head-to-head comparison in this database is limited to a single OpenCL benchmark, but that one result tells a clear story. The AMD Instinct MI100 posts a Geekbench OpenCL score of 139,035 against the NVIDIA A10M's 135,230, giving AMD a 2.8% advantage. That is a modest lead in raw compute throughput, but it is consistent with the MI100's positioning as a dedicated compute accelerator with a wider memory pipeline.

Look at the closest rivals for context. The MI100's nearest competitor is the NVIDIA Tesla V100 PCIe 16 GB, which scores 138,063, a delta of just 0.7%. The V100 SXM2 32 GB trails by 0.9%, and the AMD Radeon PRO V620 is 1.9% behind. The A10M, meanwhile, sits in a tighter cluster: the RTX 4000 Ada Generation is virtually tied at 0% delta, the Radeon PRO W6800 is 0.1% ahead, and the Radeon Pro W6800X Duo is 0.4% ahead. The A10M's 135,230 is right in the middle of that pack, while the MI100 sits slightly above its own peer group.

The 2.8% delta between the two cards is real but not transformative. In real workloads, this margin could easily be swallowed by software variance. However, the MI100's advantage is not just about the raw score; it is about how that score is achieved, which matters for specific use cases. The A10M is no slouch, it lands in the 96th percentile of all GPUs, same as the MI100, but the data shows the AMD part pulling ahead by a hair in this synthetic compute test.

Architecture Differences

The MI100 is built on AMD's CDNA 1.0 architecture, specifically the Arcturus chip, fabricated on a 7 nm TSMC process. It packs 25,600 million transistors into a 750 mm² die, yielding a transistor density of 34.1 million per square millimeter. The A10M uses NVIDIA's Ampere architecture with the GA102 chip, built on Samsung's 8 nm process. It integrates 28,300 million transistors on a 628 mm² die, achieving a higher density of 45.1 million per square millimeter. So the A10M is denser, but the MI100 has the larger physical die.

Memory is where the two diverge sharply. The MI100 uses 32 GB of HBM2 across a 4096-bit bus, delivering 1.23 TB/s of bandwidth. The A10M uses 20 GB of GDDR6 over a 320-bit bus, with 500.2 GB/s. That is a 2.46x bandwidth advantage for the MI100, which is enormous for memory-bound workloads. The A10M counters with more capacity per watt, but the raw throughput gap is decisive.

Compute resources differ too. The MI100 has 7,680 shading units, 480 TMUs, and 64 ROPs. The A10M has 7,168 shading units, 224 TMUs, and 80 ROPs. The MI100 leads in shader count and texture units, while the A10M has more ROPs. In FP32, they are nearly identical: the MI100 delivers 23.07 TFLOPS, the A10M 23.44 TFLOPS, a 1.6% edge for NVIDIA. But in FP16, the MI100 doubles to 46.14 TFLOPS (2:1 rate), while the A10M stays flat at 23.44 TFLOPS (1:1). That is a 97% advantage for the MI100 in half-precision compute, which is critical for AI training and certain scientific workloads.

The A10M brings dedicated features the MI100 lacks: 56 RT cores and 224 tensor cores. The MI100 has none. This is a fundamental architectural split, Ampere is a full-featured GPU with ray tracing and tensor acceleration, while CDNA 1.0 is a pure compute design with no graphics or tensor-specific hardware. The A10M also supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the MI100 has no API support at all (N/A for all three). The MI100 is a compute-only accelerator; the A10M can at least nominally handle graphics APIs, though it has no display outputs either.

Clock speeds differ. The MI100 runs at 1000 MHz base and 1502 MHz boost, with memory at 1200 MHz (2.4 Gbps effective). The A10M runs at 975 MHz base and 1635 MHz boost, with memory at 1563 MHz (12.5 Gbps effective). The A10M's higher boost and much faster memory clock help narrow the bandwidth gap, but the bus width difference is too large to overcome.

Power and physical specs are starkly different. The MI100 has a 300 W TDP, requires a dual-slot cooler, two 8-pin power connectors, and a 700 W suggested PSU. The A10M draws 150 W, fits in a single slot, uses one 8-pin EPS connector, and needs only a 450 W PSU. Both are 267 mm long; the MI100 is 111 mm tall, the A10M 112 mm. The A10M is an end-of-life product with a successor in Server Ada, while the MI100 is also end-of-life with no listed successor.

Where Each One Wins

The MI100 wins in memory bandwidth, FP16 compute, and raw OpenCL score. For workloads that saturate memory, large matrix operations, scientific simulation, data processing at scale, the 1.23 TB/s bandwidth is a decisive asset. The FP16 doubling to 46.14 TFLOPS makes it a stronger choice for half-precision AI training or inference where tensor cores are not specifically required. The 32 GB HBM2 capacity also gives it a capacity advantage for models that exceed 20 GB.

The A10M wins in efficiency and feature set. At half the TDP (150 W vs 300 W), it delivers nearly identical FP32 performance (23.44 vs 23.07 TFLOPS) and a higher pixel rate (130.8 vs 96.13 GPixel/s). Its 224 tensor cores provide dedicated acceleration for deep learning operations, which can be a major advantage in frameworks that leverage cuDNN and similar libraries. The 56 RT cores are irrelevant for compute, but the API support (DirectX 12 Ultimate, OpenGL 4.6, Vulkan 1.4) means it can handle tasks the MI100 simply cannot, such as any graphics or ray-tracing workload, even if those are not primary use cases.

The single-slot form factor and lower power draw make the A10M far easier to integrate into dense servers. You can fit more A10Ms per chassis or leave headroom for other components. The MI100's dual-slot, 300 W design requires more careful cooling and power planning.

In benchmark terms, the MI100 wins the only head-to-head test (1 win, 0 losses). But the A10M's rival cluster shows it is competitive with modern cards like the RTX 4000 Ada Generation (0% delta), while the MI100's rivals are older (V100 series) or less common (Radeon PRO V620). This suggests the MI100's lead is against a slightly older baseline, whereas the A10M is holding its own against current-generation hardware.

FAQ

Q: Which card has higher memory bandwidth?

A: The AMD Instinct MI100 has 1.23 TB/s of bandwidth from its HBM2 memory, while the NVIDIA A10M has 500.2 GB/s from GDDR6. The MI100's bandwidth is more than double.

Q: What is the FP32 performance difference?

A: The NVIDIA A10M delivers 23.44 TFLOPS FP32, while the AMD MI100 delivers 23.07 TFLOPS. The A10M leads by 1.6%.

Q: Does the A10M support ray tracing?

A: Yes, the NVIDIA A10M has 56 RT cores. The AMD MI100 has no RT cores and no graphics API support (DirectX, OpenGL, and Vulkan are all listed as N/A).

Q: How do they compare in FP16 compute?

A: The AMD MI100 outputs 46.14 TFLOPS at FP16 (2:1 rate), while the NVIDIA A10M outputs 23.44 TFLOPS (1:1 rate). The MI100 is 97% faster in half-precision.

Q: What are the power requirements?

A: The MI100 has a 300 W TDP with a suggested 700 W PSU, while the A10M has a 150 W TDP with a suggested 450 W PSU. The A10M uses a single 8-pin EPS connector; the MI100 uses two 8-pin connectors.

Q: Which card has more memory capacity?

A: The AMD MI100 has 32 GB of HBM2, while the NVIDIA A10M has 20 GB of GDDR6. The MI100 offers 60% more capacity.

The Verdict

The data points to a clear split. If your workloads are memory-bandwidth-hungry and operate in FP16, the AMD Instinct MI100 is the stronger choice. Its 1.23 TB/s bandwidth, 32 GB capacity, and 46.14 TFLOPS FP16 performance give it a substantial edge for scientific computing, large-scale simulations, and half-precision AI training. The 2.8% OpenCL lead over the A10M validates this in synthetic testing.

If your priority is efficiency, tensor acceleration, or any form of graphics-adjacent compute, the NVIDIA A10M is the better fit. It matches the MI100 in FP32 (23.44 TFLOPS) at half the power (150 W), fits in a single slot, and its 224 tensor cores provide hardware acceleration that the MI100 lacks entirely. The A10M also supports modern graphics APIs, making it a more versatile card despite its lower memory bandwidth.

The MI100 is a specialized instrument; the A10M is a general-purpose workhorse. Neither is universally better. The MI100 wins the single benchmark in the database, but that benchmark only measures OpenCL compute, which favors the MI100's memory architecture. For anyone running deep learning frameworks that utilize tensor cores, the A10M's feature set could outweigh its slightly lower raw score. For anyone pushing massive datasets through a 4096-bit bus, the MI100's bandwidth is non-negotiable.

Both cards are end-of-life, so the decision is likely about existing inventory or specific project requirements. The data shows the MI100 as the compute-throughput leader, and the A10M as the efficiency and feature leader. Choose accordingly.