NVIDIA A10G vs NVIDIA A10M Comparison

The NVIDIA A10G and NVIDIA A10M are both server-grade Ampere accelerators built on the GA102 chip, but the benchmark data distinguishes them clearly. In the single available head-to-head benchmark, the Geekbench OpenCL test, the A10G scores 158,063 against the A10M's 135,230, a decisive 16.9% advantage. This is the only direct comparison available, and it sets the tone for the entire analysis: the A10G is the stronger compute platform, while the A10M occupies a lower performance tier despite sharing the same architectural foundation.

Head-to-Head Benchmarks

The sole direct benchmark comparison is Geekbench OpenCL, where the A10G delivers 158,063 points and the A10M trails at 135,230. The 16.9% delta is substantial for two cards that share the same GA102 die, the same 8 nm Samsung process, and the same 150 W TDP. This margin is not a matter of minor clock tweaks; it reflects fundamental differences in the silicon configuration that propagate through every compute workload.

Contextualizing these scores against their respective nearest rivals clarifies the positioning. The A10G's average benchmark score of 151,963 places it within 1.1% of the NVIDIA Tesla V100 PCIe 32 GB (150,305), and it leads the AMD Instinct MI100 (139,035) by 9.3%. However, it trails the AMD Radeon Pro W6800X (160,671) by 5.4% and the NVIDIA A100 PCIe 40 GB (162,504) by 6.5%. The A10G is thus a strong mid-to-upper-tier server card, sitting just below the flagship A100 but comfortably above the previous-generation V100.

The A10M's average benchmark score is 135,230, which puts it in a different competitive bracket. It is statistically tied with the NVIDIA RTX 4000 Ada Generation (135,218) at a 0% delta, and it sits within 0.9% of three AMD workstation cards: the Radeon PRO W6800 (135,396, -0.1%), the Radeon Pro W6800X Duo (135,774, -0.4%), and the Radeon PRO V620 (136,472, -0.9%). The A10M is effectively the entry point for the Ampere server lineup, with performance that is competitive with current-generation workstation GPUs rather than the upper echelon of data center accelerators.

The 16.9% head-to-head gap is consistent with the raw compute specifications. The A10G's FP32 throughput is 31.52 TFLOPS, while the A10M manages 23.44 TFLOPS — a 34.5% higher peak for the A10G. The benchmark delta is smaller than the theoretical FP32 difference, which suggests that OpenCL workloads are not purely ALU-bound, but the direction is unambiguous. The A10G wins the only benchmark where both are measured, and it does so by a wide margin.

Architecture Differences

Both cards are built on the GA102 chip using the Ampere architecture, fabricated at Samsung's 8 nm process. They share the same 28,300 million transistors and a 628 mm² die size, yielding a transistor density of 45.1M per mm². The architectural lineage is identical: same generation, same chip, same process, same foundry. All differences stem from how the silicon is configured, not from the underlying design.

The most consequential divergence is in the compute core counts. The A10G features 9,216 shading units, 288 texture mapping units, and 96 render output units. The A10M is cut significantly: 7,168 shading units, 224 TMUs, and 80 ROPs. This is a 22.2% reduction in shading units and TMUs, and a 16.7% cut in ROPs. The ray tracing and tensor core counts scale accordingly — the A10G has 72 RT cores and 288 tensor cores, while the A10M has 56 RT cores and 224 tensor cores. These reductions directly explain the FP32 and FP16 performance gap, as both cards offer a 1:1 ratio between FP32 and FP16 TFLOPS.

Clock speeds further widen the gap. The A10G has a base clock of 1320 MHz and a boost clock of 1710 MHz. The A10M runs at a 975 MHz base and a 1635 MHz boost. The lower base clock on the A10M is particularly notable — it is 26.1% below the A10G's base — although the boost clocks are closer (1635 MHz vs 1710 MHz, a 4.4% difference). The A10M's substantially lower base clock means sustained workloads are likely to see a larger real-world penalty than the boost clock comparison suggests.

Memory configuration also differs significantly. The A10G carries 24 GB of GDDR6 on a 384-bit bus, delivering 600.2 GB/s of bandwidth. The A10M has 20 GB of GDDR6 on a 320-bit bus, yielding 500.2 GB/s. The memory clock is identical at 1563 MHz (12.5 Gbps effective), so the bandwidth difference is purely a function of the narrower bus. The A10G offers 20% more memory capacity and 20% more bandwidth, which matters for large model inference and dataset processing.

Other specifications are shared. Both are single-slot cards with an 8-pin EPS power connector, a 450 W suggested PSU, and a PCIe 4.0 x16 interface. Neither has display outputs, and both support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. Physical dimensions are identical: 267 mm in length and 112 mm in height. The TDP is the same 150 W for both, which means the A10G delivers its higher performance within the same power envelope — a notable efficiency advantage.

Where Each One Wins

The A10G wins every measurable category. It wins the only head-to-head benchmark, and it wins on every architectural metric: more shading units, more TMUs, more ROPs, more RT cores, more tensor cores, higher clocks, more memory, and higher bandwidth. There is no benchmark data suggesting any workload where the A10M would outperform the A10G.

The A10G's strengths are most pronounced in compute-heavy and memory-bandwidth-sensitive tasks. The 31.52 TFLOPS FP32 throughput and 600.2 GB/s bandwidth make it well-suited for AI inference, scientific computing, and rendering workloads that can utilize the full 24 GB frame buffer. Its 97th percentile ranking among all GPUs indicates that it sits near the top of the performance distribution, and its nearest rivals include the A100 and the Radeon Pro W6800X, both of which are respected data center parts.

The A10M's role is more limited. Its 96th percentile ranking is still strong, but its nearest rivals are workstation GPUs like the RTX 4000 Ada Generation and the Radeon PRO W6800, not flagship data center accelerators. The A10M offers 20 GB of memory and 500.2 GB/s of bandwidth, which is still substantial, but its 23.44 TFLOPS FP32 is a clear step down. It is best suited for workloads where the 150 W power envelope is a hard constraint and the lower compute density is acceptable.

The A10M does have one advantage: a lower base clock of 975 MHz, which may reduce idle power draw and thermal stress in lightly loaded environments, though the TDP is identical at 150 W. However, this is not a performance win; it is a design choice that favors lower sustained clocks over peak responsiveness. For users who need the absolute maximum throughput per slot, the A10G is the clear choice.

The Verdict

The data points to an unambiguous conclusion: the NVIDIA A10G is the superior accelerator. It wins the only head-to-head benchmark by 16.9%, and it leads across every architectural specification — 9,216 shading units versus 7,168, 24 GB versus 20 GB memory, 600.2 GB/s versus 500.2 GB/s bandwidth, and 31.52 TFLOPS versus 23.44 TFLOPS FP32. All of this is achieved within the same 150 W TDP and the same single-slot form factor.

For users prioritizing raw compute performance, memory capacity, or memory bandwidth, the A10G is the only rational choice. Its average benchmark score of 151,963 places it within 1.1% of the Tesla V100 PCIe 32 GB and ahead of the AMD Instinct MI100 by 9.3%, making it a credible alternative to those established accelerators. The A10G's 97th percentile ranking among all GPUs reflects its position in the upper tier of the performance hierarchy.

The A10M, by contrast, is a more specialized part. Its 135,230 average score ties it with the RTX 4000 Ada Generation and places it within 0.9% of three AMD workstation cards. For workloads that cannot utilize the A10G's full compute and memory resources, the A10M offers a lower-cost entry into the Ampere server ecosystem without sacrificing the core architecture benefits: GDDR6 memory, PCIe 4.0, and the full DirectX 12 Ultimate API support. However, its 96th percentile ranking and lower raw scores mean it is not a performance leader.

The verdict is straightforward: choose the A10G for maximum throughput, memory capacity, and bandwidth. Choose the A10M only if the lower compute configuration is sufficient for the intended workload, and the 20% less memory and 16.7% lower bandwidth are acceptable trade-offs. The data provides no scenario where the A10M outperforms the A10G.

FAQ

Q: How much faster is the A10G than the A10M in the Geekbench OpenCL test?

A: The A10G scores 158,063 versus the A10M's 135,230, a 16.9% advantage.

Q: Do the two cards use the same chip and process node?

A: Yes. Both use the GA102 chip with the Ampere architecture, fabricated on Samsung's 8 nm process with 28,300 million transistors and a 628 mm² die size.

Q: What is the memory capacity difference between the two?

A: The A10G has 24 GB of GDDR6 on a 384-bit bus, while the A10M has 20 GB on a 320-bit bus. Bandwidth is 600.2 GB/s for the A10G and 500.2 GB/s for the A10M.

Q: Which card has more CUDA cores?

A: The A10G has 9,216 shading units, 288 TMUs, and 96 ROPs, whereas the A10M has 7,168 shading units, 224 TMUs, and 80 ROPs.

Q: Is there any benchmark where the A10M wins?

A: No. The only head-to-head benchmark (Geekbench OpenCL) is won by the A10G, and the A10M has no other recorded benchmark scores that beat the A10G.

Q: How do the two cards compare to their nearest rivals?

A: The A10G is within 1.1% of the Tesla V100 PCIe 32 GB and 9.3% ahead of the AMD Instinct MI100. The A10M is statistically tied with the RTX 4000 Ada Generation at 0% delta and within 0.9% of the AMD Radeon PRO V620.