NVIDIA A10M vs NVIDIA Tesla V100 SXM2 16 GB Comparison

The NVIDIA A10M and NVIDIA Tesla V100 SXM2 16 GB represent two distinct eras of NVIDIA’s server accelerators, and the benchmark data reflects a clear generational shift. The A10M, based on the Ampere architecture, delivers a Geekbench OpenCL score of 135,230, while the Tesla V100 SXM2 16 GB, based on the older Volta architecture, scores 87,454 in the same test. This translates to a 54.6% advantage for the A10M in the only head-to-head benchmark available. The A10M also holds a higher overall percentile rank at 96 compared to the V100’s 95, indicating it outperforms a slightly larger share of all GPUs in the database.

Head-to-Head Benchmarks

The single head-to-head benchmark result overwhelmingly favors the NVIDIA A10M. In the Geekbench OpenCL test, the A10M scores 135,230 against the Tesla V100 SXM2 16 GB’s 87,454, yielding a decisive 54.6% performance gap. This is not a marginal victory; it is a substantial lead that underscores the architectural improvements built into the Ampere generation.

Looking at the A10M’s nearest rivals provides context for this score. The A10M’s 135,230 sits nearly level with the NVIDIA RTX 4000 Ada Generation (135,218, 0% delta) and the AMD Radeon PRO W6800 (135,396, -0.1% delta). It also edges out the AMD Radeon Pro W6800X Duo (135,774, -0.4%) and the AMD Radeon PRO V620 (136,472, -0.9%). In practical terms, the A10M is essentially tied with these cards in OpenCL compute, meaning its 54.6% lead over the V100 places the V100 in a significantly lower performance tier.

For the Tesla V100 SXM2 16 GB, its average benchmark score across OpenCL and Vulkan is 114,395. Its nearest rivals show a different competitive landscape: the NVIDIA RTX A5500 Mobile (113,944) is 0.4% behind, while the NVIDIA RTX 4000 SFF Ada Generation (117,088) is 2.3% ahead. The V100’s OpenCL score of 87,454 is its weak point, dragging down its average. When comparing the two cards directly, the A10M’s OpenCL result is not just faster—it is in a different class, matching modern workstation cards while the V100 lags behind.

Where Each One Wins

The benchmark data shows the NVIDIA A10M winning the only available head-to-head test, but the Tesla V100 SXM2 16 GB has its own strengths based on its specification profile. The A10M wins on raw OpenCL compute performance, a metric that often correlates with general-purpose GPU workloads like rendering, simulation, and machine learning inference. Its 54.6% lead in this test makes it the clear choice for tasks that rely on FP32 throughput.

However, the V100 has a notable advantage in memory bandwidth. It offers 897.0 GB/s of bandwidth via its HBM2 memory on a 4096-bit bus, compared to the A10M’s 500.2 GB/s on a 320-bit GDDR6 bus. For workloads that are memory-bandwidth-bound—such as large dataset traversals or certain HPC simulations—the V100’s 79.3% higher bandwidth could offset some of the A10M’s compute advantage. Additionally, the V100 provides significantly higher FP16 performance at 31.33 TFLOPS (2:1 ratio) versus the A10M’s 23.44 TFLOPS (1:1 ratio). For AI training workloads that leverage mixed-precision FP16, the V100 may be better suited despite its lower FP32 numbers.

The V100 also has more TMUs (320 vs. 224) and more ROPs (128 vs. 80), giving it higher texture fill rate (489.6 GTexel/s vs. 366.2 GTexel/s) and pixel rate (195.8 GPixel/s vs. 130.8 GPixel/s). This suggests the V100 could handle certain graphics or texture-heavy compute tasks more efficiently, even though it lacks dedicated ray tracing cores that the A10M includes.

Architecture Differences

The two cards are built on fundamentally different architectures and process nodes. The NVIDIA A10M uses the GA102 chip based on the Ampere architecture, manufactured on an 8 nm process by Samsung. It packs 28,300 million transistors on a 628 mm² die, achieving a transistor density of 45.1 million per square millimeter. In contrast, the Tesla V100 SXM2 16 GB uses the GV100 chip based on the Volta architecture, built on TSMC’s 12 nm process. It contains 21,100 million transistors on a larger 815 mm² die, resulting in a lower density of 25.9 million per square millimeter.

The A10M’s newer process allows for higher clock speeds at lower power. It runs at a base clock of 975 MHz and boosts to 1635 MHz, while the V100 starts higher at 1312 MHz but only boosts to 1530 MHz. Despite the V100’s higher base clock, the A10M’s boost clock is 6.9% faster, and it achieves this while consuming only 150 W TDP versus the V100’s 250 W TDP. The A10M also uses a standard 8-pin EPS power connector, while the V100 SXM2 module has no power connectors, relying on the SXM socket for power delivery.

Memory architecture differs significantly. The A10M uses 20 GB of GDDR6 on a 320-bit bus, while the V100 uses 16 GB of HBM2 on a 4096-bit bus. The V100’s memory runs at a lower effective speed of 1752 Mbps compared to the A10M’s 12.5 Gbps, but the wider bus gives the V100 nearly double the bandwidth. The A10M has more shading units (7168 vs. 5120) and more tensor cores (224 vs. 640? Note: the A10M lists 224 tensor cores, while the V100 lists 640). The V100’s 640 tensor cores are a distinct advantage for deep learning, while the A10M adds 56 RT cores that the V100 lacks.

FAQ

Q: Which GPU has higher FP32 compute performance?

A: The NVIDIA A10M has 23.44 TFLOPS of FP32 performance, while the Tesla V100 SXM2 16 GB has 15.67 TFLOPS. This gives the A10M a 49.6% advantage in single-precision floating-point throughput.

Q: How does memory bandwidth compare between the two?

A: The Tesla V100 SXM2 16 GB offers 897.0 GB/s of memory bandwidth, which is significantly higher than the A10M’s 500.2 GB/s. This is due to the V100’s 4096-bit HBM2 interface versus the A10M’s 320-bit GDDR6 bus.

Q: Does the A10M support ray tracing?

A: Yes, the NVIDIA A10M includes 56 dedicated RT cores as part of its Ampere architecture. The Tesla V100 SXM2 16 GB does not list any RT cores, as Volta predates NVIDIA’s ray tracing hardware.

Q: What are the power requirements for each card?

A: The A10M has a 150 W TDP and requires a 450 W power supply, while the V100 SXM2 has a 250 W TDP with a recommended 600 W power supply. The A10M uses a standard 8-pin EPS connector, while the V100 SXM2 module has no power connectors.

Q: Which card has better performance in the Geekbench OpenCL test?

A: The NVIDIA A10M scores 135,230, which is 54.6% higher than the Tesla V100 SXM2 16 GB’s score of 87,454. The A10M wins this head-to-head benchmark decisively.

Q: Are both cards still in production?

A: No, both the NVIDIA A10M and the Tesla V100 SXM2 16 GB are marked as end-of-life products. The V100 was released on 2017-06-20, while the A10M has no recorded release date in the data.

Specification Differences

The two cards differ across nearly every major specification category. The most striking difference is in process technology: the A10M uses an 8 nm node from Samsung, while the V100 uses TSMC’s 12 nm node. This leads to different transistor counts (28,300 million for the A10M vs. 21,100 million for the V100) and die sizes (628 mm² vs. 815 mm²). The A10M achieves a higher transistor density of 45.1M/mm² versus the V100’s 25.9M/mm².

Clock speeds also diverge. The A10M has a lower base clock (975 MHz vs. 1312 MHz) but a higher boost clock (1635 MHz vs. 1530 MHz). Memory configurations are entirely different: the A10M has 20 GB of GDDR6 at 500.2 GB/s, while the V100 has 16 GB of HBM2 at 897.0 GB/s. The compute unit counts favor the A10M in shading units (7168 vs. 5120) and RT cores (56 vs. 0), but the V100 has more TMUs (320 vs. 224), ROPs (128 vs. 80), and tensor cores (640 vs. 224).

Power and form factor differ substantially. The A10M is a single-slot card with a 150 W TDP, while the V100 is an SXM module with a 250 W TDP. The A10M uses a PCIe 4.0 x16 interface, while the V100 uses PCIe 3.0 x16. Both cards have no display outputs. The A10M has defined dimensions (267 mm length, 112 mm height), while the V100’s dimensions are not specified. API support also differs: the A10M supports DirectX 12 Ultimate (12_2), while the V100 is limited to DirectX 12 (12_1).

The Verdict

The data clearly favors the NVIDIA A10M for general compute and FP32-heavy workloads. Its 54.6% lead in OpenCL benchmarking, combined with higher boost clocks, more shading units, and a newer architecture, makes it the superior choice for tasks like rendering, simulation, and standard GPU compute. The A10M also achieves this with lower power consumption (150 W vs. 250 W) and a more compact single-slot form factor, which is advantageous for dense server deployments.

However, the Tesla V100 SXM2 16 GB retains relevance for specific use cases. Its 897.0 GB/s memory bandwidth is 79.3% higher than the A10M’s, making it better suited for memory-bound applications. Its FP16 performance of 31.33 TFLOPS exceeds the A10M’s 23.44 TFLOPS, which could be critical for certain AI training workloads that utilize mixed precision. The V100 also has 640 tensor cores, nearly three times the A10M’s 224, making it a potentially stronger choice for deep learning inference tasks that are optimized for Volta’s tensor core architecture.

For most users, the A10M is the better investment due to its raw performance and efficiency. The benchmark data shows it is not only faster than the V100 in OpenCL but also competitive with modern workstation GPUs like the RTX 4000 Ada Generation. The V100 should only be considered if a workload specifically requires HBM2 bandwidth or leverages the higher FP16 throughput and tensor core count. Otherwise, the A10M’s 54.6% performance advantage in the head-to-head test is too large to ignore.