GPU Comparison

The NVIDIA A10M and the NVIDIA RTX 4000 SFF Ada Generation are both 20 GB workstation-class accelerators, yet they represent two distinct design philosophies from different architectural eras. The A10M is an Ampere-generation server part built for maximum compute throughput in a single-slot form factor, while the RTX 4000 SFF is an Ada Lovelace-generation workstation card engineered for extreme power efficiency and compact size. Benchmark and specification data from the FACT PACK reveals a close contest in raw compute, but significant divergences in architecture, physical design, and performance characteristics.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, where the two cards are nearly inseparable. The NVIDIA A10M scores 135230, while the NVIDIA RTX 4000 SFF Ada Generation scores 124812. This gives the A10M a win with a delta of 8.3 percent. While 8.3 percent is a meaningful margin, it is far from a knockout blow. The A10M’s advantage in this test stems from its higher raw compute specifications: it offers 7168 shading units versus 6144 on the RTX 4000 SFF, and a boost clock of 1635 MHz compared to 1560 MHz. These factors combine to deliver 23.44 TFLOPS of FP32 performance on the A10M, against 19.17 TFLOPS on the RTX 4000 SFF. In practical terms, the A10M is roughly 22 percent ahead in theoretical FP32 throughput, yet the OpenCL score gap is only 8.3 percent, suggesting that the RTX 4000 SFF’s newer architecture extracts more efficiency from each FLOP.

Looking at the broader context of the nearest rivals, both cards occupy the same performance tier. The A10M’s percentile ranking is 96, and its average benchmark score of 135230 places it in a tight cluster. Its nearest rival, the NVIDIA RTX 4000 Ada Generation (the non-SFF variant), scores 135218, a delta of 0 percent. The AMD Radeon PRO W6800 is 0.1 percent ahead, and the AMD Radeon Pro W6800X Duo is 0.4 percent ahead. The RTX 4000 SFF Ada Generation, with a percentile ranking of 95, has an average benchmark score of 117088, which is pulled down by its additional Vulkan result of 109364. Its nearest rival, the NVIDIA GB10, scores 117393, a delta of -0.3 percent, while the AMD Radeon PRO W7700 is 1.6 percent behind. This data indicates that while the A10M wins the head-to-head OpenCL test, both cards are within a competitive band where architectural differences, not raw scores, will differentiate them for specific workloads.

FAQ

Q: Which card has a higher benchmark score in the head-to-head comparison?

A: The NVIDIA A10M wins the Geekbench OpenCL test with a score of 135230, compared to the RTX 4000 SFF Ada Generation’s 124812, a delta of 8.3 percent.

Q: How does the RTX 4000 SFF Ada Generation compare to its nearest rival in average benchmark score?

A: The RTX 4000 SFF Ada Generation has an average benchmark score of 117088. Its nearest rival, the NVIDIA GB10, scores 117393, which is 0.3 percent higher. The AMD Radeon PRO W7700 is 1.6 percent behind the RTX 4000 SFF.

Q: What is the memory capacity and type for both cards?

A: Both the NVIDIA A10M and the NVIDIA RTX 4000 SFF Ada Generation feature 20 GB of GDDR6 memory.

Q: What is the power consumption difference between the two cards?

A: The NVIDIA A10M has a TDP of 150 W, while the NVIDIA RTX 4000 SFF Ada Generation has a TDP of 70 W. This means the RTX 4000 SFF uses less than half the power of the A10M.

Q: How do their memory bandwidth figures compare?

A: The NVIDIA A10M has a memory bandwidth of 500.2 GB/s, which is significantly higher than the RTX 4000 SFF Ada Generation’s 280.0 GB/s.

Q: What is the production status of each card?

A: The NVIDIA A10M is marked as end-of-life, while the NVIDIA RTX 4000 SFF Ada Generation is listed as active, with a release date of 2023-03-20.

Architecture Differences

The architectural divide between these two cards is stark. The NVIDIA A10M is built on the GA102 chip using the Ampere architecture, fabricated on Samsung’s 8 nm process. This is a large, power-hungry design: the die size is 628 mm², housing 28,300 million transistors, which yields a transistor density of 45.1M per mm². The RTX 4000 SFF Ada Generation, in contrast, uses the AD104 chip with the Ada Lovelace architecture, built on TSMC’s 5 nm process. This allows for a much smaller die of 294 mm², yet it packs 35,800 million transistors, resulting in a transistor density of 121.8M per mm². The RTX 4000 SFF’s density is nearly triple that of the A10M, which explains its superior efficiency.

The compute resources also differ. The A10M fields 7168 shading units, 224 texture mapping units, and 80 ROPs. It also includes 56 ray tracing cores and 224 tensor cores. The RTX 4000 SFF Ada Generation is more conservative: 6144 shading units, 192 TMUs, and 64 ROPs, with 48 ray tracing cores and 192 tensor cores. In every category, the A10M has more hardware, which is why it achieves higher peak rates: 130.8 GPixel/s pixel rate and 366.2 GTexel/s texture rate, versus 99.84 GPixel/s and 299.5 GTexel/s on the RTX 4000 SFF. However, the Ada Lovelace architecture in the RTX 4000 SFF brings newer feature support, including a more modern ray tracing implementation, which is not fully captured by the OpenCL benchmark.

Specification Differences

The specification sheets reveal several key differences beyond the compute cores. The most obvious is the memory subsystem. The A10M uses a 320-bit memory bus, enabling a bandwidth of 500.2 GB/s at a memory clock of 1563 MHz (12.5 Gbps effective). The RTX 4000 SFF Ada Generation uses a much narrower 160-bit bus, which limits bandwidth to 280.0 GB/s even though its memory clock is higher at 1750 MHz (14 Gbps effective). For memory-bound workloads, the A10M has a clear theoretical advantage.

Physical specifications also diverge significantly. The A10M is a single-slot card that requires an 8-pin EPS power connector, with a suggested PSU of 450 W. Its dimensions are 267 mm in length and 112 mm in height. The RTX 4000 SFF Ada Generation is a dual-slot card that draws power entirely from the PCIe slot, requiring no external power connectors and a suggested PSU of only 250 W. It is substantially smaller, measuring 168 mm in length and 69 mm in height. Display outputs also differ: the A10M has no display outputs, positioning it as a pure compute accelerator, while the RTX 4000 SFF includes 4x mini-DisplayPort 1.4a, making it suitable for workstation visualization tasks.

The TDP gap is enormous: the A10M is rated at 150 W, while the RTX 4000 SFF is rated at 70 W. This is a direct consequence of the process node difference (8 nm vs 5 nm) and the smaller die on the RTX 4000 SFF. The production status also differs, with the A10M being end-of-life and the RTX 4000 SFF being active.

The Verdict

The data supports a clear conclusion: these are two different tools for two different jobs. The NVIDIA A10M is the performance leader in the head-to-head OpenCL benchmark, winning by 8.3 percent. It achieves this through a larger memory bus, higher bandwidth, and more compute units. For applications that are throughput-bound and can tolerate a 150 W power draw and a long single-slot card, the A10M is the stronger choice. Its 20 GB of GDDR6 memory and 500.2 GB/s bandwidth make it well-suited for large datasets that require fast streaming.

The NVIDIA RTX 4000 SFF Ada Generation, however, is the more logical choice for environments where power and space are constrained. Its 70 W TDP is less than half the A10M’s, and its compact 168 mm length (versus 267 mm) allows it to fit in small form factor workstations. It also offers display outputs, which the A10M lacks entirely. While it trails the A10M in raw OpenCL score by 8.3 percent, its newer Ada Lovelace architecture and higher transistor density (121.8M per mm² vs 45.1M per mm²) suggest better architectural efficiency per watt. The RTX 4000 SFF is also an active product, whereas the A10M is end-of-life, which may impact long-term driver support and availability.

Where Each One Wins

The NVIDIA A10M wins in scenarios that demand maximum raw compute and memory bandwidth. Its 500.2 GB/s bandwidth is 79 percent higher than the RTX 4000 SFF’s 280.0 GB/s, which is critical for large model training or high-resolution rendering tasks that stream data continuously. The A10M’s higher FP32 throughput (23.44 TFLOPS vs 19.17 TFLOPS) and larger number of tensor cores (224 vs 192) also give it an edge in matrix-heavy operations like deep learning inference. The single-slot design is an advantage in dense server configurations where multiple accelerators are packed into a chassis, provided that 150 W per card can be dissipated.

The NVIDIA RTX 4000 SFF Ada Generation wins in power-constrained and space-constrained environments. Its 70 W TDP means it can often run in systems with a 250 W power supply, opening up options for compact workstations that cannot accommodate the A10M’s 450 W suggested PSU or its 267 mm length. The inclusion of 4x mini-DisplayPort 1.4a outputs makes it suitable for interactive visualization, CAD, and content creation workflows where a display is needed. The dual-slot form factor, while wider, is shorter and lower-profile, fitting into smaller chassis. Data shows the RTX 4000 SFF also has a Vulkan score of 109364, indicating it can handle graphics APIs, while the A10M has no such benchmark recorded. For users prioritizing energy efficiency, low noise, or small physical footprint without sacrificing the 20 GB memory capacity, the RTX 4000 SFF Ada Generation is the clear winner.