NVIDIA A10M vs NVIDIA Tesla V100 PCIe 32 GB Comparison

The NVIDIA Tesla V100 PCIe 32 GB and the NVIDIA A10M are two end-of-life server accelerators aimed at very different workload profiles, despite both holding the 96th percentile ranking among all GPUs. The data shows a clear separation: the V100 leads in raw compute and memory bandwidth, while the A10M counters with a newer architecture, higher FP32 throughput, and a dramatically more efficient power envelope. Based strictly on benchmark results, the V100 is the choice for compute-heavy tasks where memory capacity and bandwidth are paramount, whereas the A10M is positioned for environments prioritizing power efficiency and raw FP32 throughput per watt. The single head-to-head result shows the V100 winning the Geekbench OpenCL test by 24.8%, but the A10M’s architectural advantages and lower power draw make it a compelling option for specific deployment scenarios.

The Verdict

The benchmark data presents a decisive, if narrow, outcome. In the only direct comparison available, the Geekbench OpenCL test, the Tesla V100 scores 168,763 against the A10M’s 135,230, giving the V100 a 24.8% lead. This is a substantial margin, indicating that for general-purpose compute workloads as measured by OpenCL, the older Volta architecture holds a clear performance advantage over the Ampere-based A10M.

However, the verdict is not a simple landslide. The A10M’s average benchmark score of 135,230 places it in a tight cluster with its nearest rivals, including the NVIDIA RTX 4000 Ada Generation (deltaPct of 0) and the AMD Radeon PRO W6800 (deltaPct of -0.1). This suggests the A10M is a solid, mid-pack performer. The V100’s average score of 150,305, meanwhile, positions it closer to the AMD Instinct MI100 (which is 8.1% slower) and the NVIDIA A100 PCIe 40 GB (which is 7.5% faster). Data implies the V100 sits in a higher performance tier than the A10M, despite its age. For buyers, the choice hinges on whether the 24.8% OpenCL performance lead justifies the V100’s significantly higher power consumption of 250 W versus the A10M’s 150 W, and its much larger dual-slot footprint versus the A10M’s single-slot design.

FAQ

Q: Which GPU is faster in the head-to-head benchmark?

A: The NVIDIA Tesla V100 PCIe 32 GB wins the only direct benchmark comparison. It scored 168,763 in Geekbench OpenCL, which is 24.8% higher than the NVIDIA A10M’s 135,230.

Q: How does the A10M compare to its closest competitors?

A: The A10M’s average benchmark score of 135,230 is essentially tied with the NVIDIA RTX 4000 Ada Generation (0% delta) and the AMD Radeon PRO W6800 (-0.1% delta). It is 0.4% faster than the AMD Radeon Pro W6800X Duo and 0.9% faster than the AMD Radeon PRO V620.

Q: What are the key architectural differences between these two cards?

A: The V100 is built on the 12 nm Volta architecture (GV100 chip) from TSMC, while the A10M uses the 8 nm Ampere architecture (GA102 chip) from Samsung. The V100 features 640 tensor cores, while the A10M has 224 tensor cores and adds 56 ray tracing cores, a feature the V100 lacks entirely.

Q: Which card has a higher memory bandwidth?

A: The Tesla V100 has a massive advantage here. It features 32 GB of HBM2 memory on a 4096-bit bus, delivering 897.0 GB/s of bandwidth. The A10M has 20 GB of GDDR6 memory on a 320-bit bus, providing 500.2 GB/s.

Q: Does the newer A10M have a higher FP32 compute performance?

A: Yes. The A10M delivers 23.44 TFLOPS of FP32 performance, which is significantly higher than the V100’s 14.13 TFLOPS. This is a 65.8% advantage for the A10M in raw single-precision floating-point throughput.

Q: What are the power and physical size differences?

A: The A10M is far more efficient and compact. It has a 150 W TDP and a single-slot design (267 mm in length), whereas the V100 has a 250 W TDP and requires a dual-slot form factor. The A10M also has a lower suggested PSU of 450 W versus 600 W for the V100.

Architecture Differences

The architectural gap between these two is generational and significant. The Tesla V100 is built on the Volta architecture, utilizing the GV100 chip manufactured on a 12 nm process at TSMC. This older design employs a massive 815 mm² die containing 21,100 million transistors, resulting in a transistor density of 25.9 million per mm². In contrast, the A10M is based on the Ampere architecture, using the GA102 chip fabricated on an 8 nm process at Samsung. This newer node allows for a smaller 628 mm² die but packs far more transistors—28,300 million—achieving a density of 45.1 million per mm².

The compute cores tell a story of divergent design philosophies. The V100 relies on 5,120 shading units, 320 texture mapping units (TMUs), and 128 render output units (ROPs). It includes 640 tensor cores dedicated to AI workloads but has no ray tracing cores. The A10M, on the other hand, sports 7,168 shading units but fewer TMUs (224) and ROPs (80). It integrates 224 tensor cores and adds 56 ray tracing cores, marking a significant feature addition that the Volta architecture simply does not possess. This suggests the A10M is designed to handle real-time ray tracing workloads, a capability absent from the V100.

Memory architecture is another fundamental divergence. The V100 uses HBM2 memory on a 4096-bit bus, a configuration designed for maximum bandwidth. The A10M opts for GDDR6 on a 320-bit bus, which trades bandwidth for cost and simplicity. The implications are clear: the V100 is built for data movement-heavy tasks, while the A10M prioritizes compute density. The A10M’s FP16 performance of 23.44 TFLOPS (1:1) matches its FP32 output, whereas the V100’s FP16 of 28.26 TFLOPS is achieved via a 2:1 ratio, effectively doubling its FP32 rate. This indicates a different optimization target for mixed-precision work.

Specification Differences

The specification sheet reveals a stark contrast in priorities. The Tesla V100 launches with a base clock of 1230 MHz and a boost clock of 1380 MHz, while the A10M starts lower at 975 MHz but boosts much higher to 1635 MHz. This higher boost clock contributes to the A10M’s superior FP32 throughput of 23.44 TFLOPS versus the V100’s 14.13 TFLOPS. Pixel and texture rates also differ: the V100 achieves 176.6 GPixel/s and 441.6 GTexel/s, while the A10M manages 130.8 GPixel/s and 366.2 GTexel/s, respectively.

Memory specifications are where the V100 reasserts dominance. It offers 32 GB of HBM2 with 897.0 GB/s bandwidth, a stark contrast to the A10M’s 20 GB GDDR6 with 500.2 GB/s. The bus widths—4096-bit versus 320-bit—explain this disparity. Power consumption is inverted; the A10M draws 150 W versus the V100’s 250 W, and its 8-pin EPS connector and 450 W suggested PSU are modest compared to the V100’s dual 8-pin connectors and 600 W PSU requirement. The A10M is also physically smaller: it is a single-slot card measuring 267 mm in length and 112 mm in height, whereas the V100 is a dual-slot card with no listed dimensions.

Interface support also differs. The A10M utilizes PCIe 4.0 x16, doubling the bandwidth of the V100’s PCIe 3.0 x16 interface. The A10M also supports DirectX 12 Ultimate (12_2), while the V100 is limited to DirectX 12 (12_1). Both cards have no display outputs, confirming their server-only designation. The V100 was released on March 26, 2018, while the A10M has no release date listed. The V100’s predecessor is Tesla Pascal and its successor is Tesla Turing, whereas the A10M succeeds Tesla Turing and is followed by Server Ada.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, and it delivers a decisive result for the Tesla V100. The V100 scores 168,763, while the A10M manages 135,230. This represents a 24.8% performance delta in favor of the V100. This is a substantial gap that cannot be ignored, suggesting that for general-purpose compute tasks measured by OpenCL, the Volta architecture’s massive memory bandwidth and high-bandwidth HBM2 implementation provide a significant real-world advantage over the Ampere card.

The implications of this score are amplified when considering the V100’s position among its rivals. Its average benchmark score of 150,305 places it in a league above the A10M, with the nearest competitor being the AMD Instinct MI100, which is 8.1% slower. The NVIDIA A100 PCIe 40 GB is 7.5% faster, showing the V100 is competitive even against newer flagship parts. The A10M’s average score of 135,230 is tightly clustered with its own rivals—the NVIDIA RTX 4000 Ada Generation is exactly 0% different, and the AMD Radeon PRO W6800 is 0.1% slower. This indicates that while the A10M is a competent performer, it does not reach the performance tier occupied by the V100.

The deltaPct values from the nearest rivals provide further context. The V100 is 1.1% slower than the NVIDIA A10G, but 6.5% slower than the AMD Radeon Pro W6800X. The A10M, meanwhile, is 0.4% faster than the AMD Radeon Pro W6800X Duo and 0.9% faster than the AMD Radeon PRO V620. These numbers paint a picture of two cards in different performance strata, with the V100 consistently outperforming the A10M in aggregate compute benchmarks, despite the latter’s newer architecture.

Where Each One Wins

The Tesla V100 PCIe 32 GB is the clear winner in scenarios demanding maximum memory throughput and capacity. Its 32 GB of HBM2 memory on a 4096-bit bus, delivering 897.0 GB/s, is more than 79% higher bandwidth than the A10M’s 500.2 GB/s. This makes the V100 the superior choice for workloads that are memory-bound, such as large-scale data analytics, scientific simulations, and deep learning training on massive datasets. The 24.8% OpenCL benchmark victory reinforces its strength in general-purpose compute, and its pixel rate of 176.6 GPixel/s and texture rate of 441.6 GTexel/s are both higher than the A10M’s, indicating better fill-rate performance.

The NVIDIA A10M wins decisively in power efficiency and raw FP32 compute density. Its 23.44 TFLOPS of FP32 performance is 65.8% higher than the V100’s 14.13 TFLOPS, achieved while drawing only 150 W compared to the V100’s 250 W. This makes the A10M a compelling option for high-density server deployments where power and cooling are limiting factors. Its single-slot design (267 mm x 112 mm) and PCIe 4.0 interface also make it easier to integrate into modern systems. The A10M’s 56 ray tracing cores and DirectX 12 Ultimate support give it a unique capability for ray-traced rendering workloads, a feature the V100 cannot offer. For FP16 workloads, the A10M’s 1:1 ratio (23.44 TFLOPS) provides consistent performance, whereas the V100’s 2:1 ratio (28.26 TFLOPS) delivers higher peak but at a different power profile.

The use-case split is therefore clear: the V100 for memory-intensive compute and legacy compatibility, and the A10M for power-constrained, FP32-heavy, or ray-tracing-enabled tasks. The data does not support a universal recommendation, but rather a workload-specific one.