AMD FirePro S10000 vs NVIDIA A2 Comparison

The NVIDIA A2 and AMD FirePro S10000 represent two very different approaches to workstation computing, separated by nearly a decade of architectural evolution. The data shows a clear split: the A2 dominates in OpenCL compute workloads, while the older FirePro S10000 edges out a narrow victory in Vulkan. For users prioritizing modern compute acceleration and efficiency, the A2 is the logical choice, as it delivers a 15.4% higher OpenCL score while consuming a fraction of the power. Conversely, the FirePro S10000, despite its age, remains competitive in Vulkan-based tasks, making it a niche option for legacy software stacks that favor its GCN 1.0 architecture. The A2 also offers a massive memory capacity advantage at 16 GB versus 3 GB, which is critical for large datasets. Ultimately, the A2 is the superior all-around card for contemporary workloads, while the S10000 is only relevant for specific, Vulkan-centric scenarios where its slight 0.4% edge matters.

The Verdict

From a pure performance standpoint, the NVIDIA A2 is the winner in compute-heavy applications. Its OpenCL score of 35,357 is 15.4% higher than the FirePro S10000's 30,631, a substantial margin that indicates better raw throughput for general-purpose GPU computing. The A2's average benchmark score of 34,690 also places it in the 79th percentile of all GPUs, compared to the S10000's 77th percentile and average score of 32,388. This means the A2 outperforms roughly 79% of all GPUs, while the S10000 trails slightly.

However, the Vulkan results tell a different story. The FirePro S10000 scores 34,145, narrowly beating the A2's 34,023 by just 0.4%. This is a negligible margin, but it shows that the older card is not entirely obsolete. For users whose primary applications rely on Vulkan, the S10000 could be a viable option, though the difference is within a margin of error. The A2's nearest rivals include the NVIDIA T1000 8 GB and AMD Radeon HD 7970, both with average scores around 34,500, indicating that the A2 sits in a competitive mid-range tier. The S10000, meanwhile, is closely matched with the AMD Radeon RX 7900 GRE, which scores 32,456, just 0.2% higher.

The verdict is straightforward: choose the A2 for modern compute tasks, AI inference, and memory-intensive workloads. Choose the FirePro S10000 only if your software is locked to Vulkan and you require its specific feature set, accepting its higher power draw and limited memory. The data does not support the S10000 as a general-purpose recommendation.

Architecture Differences

The architectural gap between these two cards is vast. The NVIDIA A2 is built on the Ampere architecture with the GA107 chip, fabricated on an 8 nm process by Samsung. It packs 8,700 million transistors onto a 200 mm² die, yielding a transistor density of 43.5 million per square millimeter. In contrast, the AMD FirePro S10000 uses the GCN 1.0 architecture with the Tahiti chip, manufactured on a 28 nm process by TSMC. It contains 4,313 million transistors on a larger 352 mm² die, resulting in a much lower density of 12.3 million per square millimeter.

The A2 features 1,280 shading units, 40 texture mapping units, and 32 ROPs, along with 10 RT cores and 40 tensor cores. This hardware support for ray tracing and tensor operations is entirely absent on the S10000, which has 1,792 shading units, 112 TMUs, and 32 ROPs but no dedicated RT or tensor cores. The A2's tensor cores are particularly significant for AI and machine learning workloads, giving it a capability the S10000 simply cannot match.

Memory architectures differ fundamentally. The A2 uses 16 GB of GDDR6 on a 128-bit bus, delivering 200.1 GB/s of bandwidth. The S10000 uses 3 GB of GDDR5 on a 384-bit bus, which provides a higher bandwidth of 240.0 GB/s despite the smaller capacity. This is an interesting inversion: the older card has faster memory bandwidth but much less capacity. The A2's boost clock of 1,770 MHz is nearly double the S10000's 950 MHz, contributing to its higher FP32 throughput of 4.531 TFLOPS versus 3.405 TFLOPS for the S10000.

Power and physical requirements also diverge sharply. The A2 has a 60 W TDP and requires no power connectors, fitting in a single slot. The S10000 has a 375 W TDP, requires two 8-pin connectors, occupies a dual-slot form factor, and measures 305 mm in length. The A2 also supports PCIe 4.0 x8, while the S10000 uses PCIe 3.0 x16. Finally, the S10000 has display outputs (1x DVI and 4x mini-DisplayPort 1.2), while the A2 has no outputs, indicating its purpose as a compute-only accelerator.

FAQ

Q: Which card has higher raw compute performance?

A: The NVIDIA A2 delivers 4.531 TFLOPS of FP32 performance, which is 33% higher than the AMD FirePro S10000's 3.405 TFLOPS. This aligns with the A2's 15.4% lead in the OpenCL benchmark.

Q: Does the AMD FirePro S10000 have any advantage over the A2?

A: Yes, in Vulkan benchmarks, the S10000 scores 34,145, slightly edging out the A2's 34,023 by 0.4%. It also has higher memory bandwidth at 240.0 GB/s compared to the A2's 200.1 GB/s.

Q: What is the memory capacity difference?

A: The NVIDIA A2 has 16 GB of GDDR6 memory, while the AMD FirePro S10000 has only 3 GB of GDDR5. This is a 5.3x difference, making the A2 far more suitable for large datasets.

Q: Can the FirePro S10000 handle ray tracing?

A: No, the S10000 has no RT cores. The NVIDIA A2 includes 10 RT cores and 40 tensor cores, enabling hardware-accelerated ray tracing and AI operations that the S10000 cannot perform.

Q: What are the power requirements for each card?

A: The A2 has a 60 W TDP with no power connectors and a suggested PSU of 250 W. The S10000 has a 375 W TDP, requires two 8-pin connectors, and needs a 750 W suggested PSU.

Q: Which card is better for AI workloads?

A: The NVIDIA A2 is clearly superior due to its 40 tensor cores, which are designed for tensor operations common in AI inference. The S10000 lacks any such hardware, making it unsuitable for modern AI tasks.

Specification Differences

The two cards differ across nearly every major specification field. The process node is 8 nm for the A2 versus 28 nm for the S10000, reflecting the generational leap. Transistor counts are 8,700 million versus 4,313 million, and die sizes are 200 mm² versus 352 mm². The A2's base clock is 1,440 MHz with a boost of 1,770 MHz, while the S10000 runs at 825 MHz base and 950 MHz boost. Memory speed is 12.5 Gbps effective for the A2 versus 5 Gbps for the S10000, with capacities of 16 GB GDDR6 and 3 GB GDDR5 respectively. Bus widths are 128-bit versus 384-bit, and bandwidth is 200.1 GB/s versus 240.0 GB/s.

Compute unit counts vary: the A2 has 1,280 shading units, 40 TMUs, and 32 ROPs, while the S10000 has 1,792 shading units, 112 TMUs, and 32 ROPs. The A2 uniquely features 10 RT cores and 40 tensor cores, which the S10000 lacks entirely. Pixel rates are 56.64 GPixel/s for the A2 versus 30.40 GPixel/s for the S10000, while texture rates are 70.80 GTexel/s versus 106.4 GTexel/s. FP32 performance is 4.531 TFLOPS for the A2 versus 3.405 TFLOPS, and the A2 supports FP16 at 1:1 ratio while the S10000 has no listed FP16 capability.

Power delivery differs drastically: 60 W TDP with no connectors for the A2 versus 375 W with two 8-pin connectors for the S10000. The A2 is single-slot, while the S10000 is dual-slot and 305 mm long. Bus interfaces are PCIe 4.0 x8 versus PCIe 3.0 x16. The A2 has no display outputs, while the S10000 offers 1x DVI and 4x mini-DisplayPort 1.2. API support also differs: the A2 supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the S10000 is limited to DirectX 12 (11_1) and Vulkan 1.2.170.

Head-to-Head Benchmarks

The benchmark data presents a split decision. In Geekbench OpenCL, the NVIDIA A2 scores 35,357, decisively beating the AMD FirePro S10000's 30,631 by 15.4%. This is a significant margin that reflects the A2's architectural advantages in compute-heavy tasks. The A2's score places it close to rivals like the NVIDIA T1000 8 GB (34,561) and AMD Radeon HD 7970 (34,541), but it clearly outpaces the S10000.

In Geekbench Vulkan, however, the tables turn slightly. The FirePro S10000 scores 34,145, edging out the A2's 34,023 by a narrow 0.4%. This is a very tight margin, and it is notably the S10000's Vulkan score is actually higher than its OpenCL score, while the A2's Vulkan score is lower than its OpenCL score. This suggests the S10000's GCN architecture handles Vulkan workloads more efficiently relative to OpenCL.

The overall win tally is even at one win each, but the magnitude of the A2's OpenCL victory (15.4%) far exceeds the S10000's Vulkan margin (0.4%). The A2's average benchmark score of 34,690 versus 32,388 for the S10000 reinforces its overall superiority. In terms of percentiles, the A2 ranks in the 79th percentile of all GPUs, while the S10000 sits in the 77th percentile.

Where Each One Wins

The NVIDIA A2 wins decisively in OpenCL compute workloads, delivering 15.4% higher performance than the FirePro S10000. This makes it the better choice for general-purpose GPU computing, scientific simulations, and data processing tasks that leverage OpenCL. The A2's 16 GB memory capacity is a major advantage for workloads that need to hold large models or datasets in VRAM, as the S10000's 3 GB would quickly become a bottleneck. The A2's tensor cores also give it a unique edge in AI inference and machine learning, areas where the S10000 has no hardware support. Additionally, the A2's 60 W TDP and single-slot design make it far easier to deploy in dense server environments, where power and space are at a premium.

The AMD FirePro S10000 wins narrowly in Vulkan, with a 0.4% edge over the A2. This makes it a candidate for applications that are specifically optimized for Vulkan and cannot use OpenCL. The S10000 also offers higher memory bandwidth at 240.0 GB/s, which could benefit certain bandwidth-sensitive Vulkan workloads despite the smaller capacity. Its 1,792 shading units and 112 TMUs provide higher texture fill rates (106.4 GTexel/s) than the A2, which could be relevant for specific rendering tasks. The S10000's display outputs also allow it to function as a traditional GPU with monitor connectivity, something the A2 cannot do. However, these advantages are limited to niche scenarios, and the S10000's 375 W TDP and dual-slot requirement make it less practical for modern installations. For most users, the A2 is the superior choice, with its massive memory, modern architecture, and compute performance.