NVIDIA A10M vs NVIDIA Tesla V100 PCIe 16 GB Comparison

The NVIDIA Tesla V100 PCIe 16 GB and the NVIDIA A10M represent two distinct generations of datacenter compute, separated by architecture philosophy and market positioning. The V100, built on the Volta architecture, is an end-of-life product from 2017, while the A10M, based on the Ampere architecture, is also end-of-life but targets a different power and efficiency envelope. The benchmark data, while limited to a single shared workload, provides a clear statistical picture: the V100 holds a decisive lead in raw compute throughput in the tested OpenCL workload, but the A10M counters with a significantly lower power draw, a larger memory pool, and support for modern API features. This comparison hinges on whether the priority is peak compute performance or operational efficiency and memory capacity.

Where Each One Wins

The NVIDIA Tesla V100 PCIe 16 GB is the unequivocal winner in raw compute performance, based on the available head-to-head benchmark. In the Geekbench OpenCL test, the V100 scores 163,063 points, which is 20.6% higher than the A10M's 135,230 points. This substantial margin indicates that for compute-bound workloads that scale with raw FP32 and FP16 throughput, the V100 is the more capable processor. The V100's architecture is designed for high-intensity floating-point operations, evidenced by its FP32 rating of 14.13 TFLOPS and its FP16 rating of 28.26 TFLOPS (2:1). This makes it the preferred choice for tasks where maximum mathematical throughput is the sole criterion, such as large-scale matrix multiplications in scientific simulation or deep learning training loops that can utilize its 640 Tensor Cores.

The NVIDIA A10M wins on operational efficiency and memory capacity. While it trails in raw compute, it achieves this with a TDP of just 150 W, exactly half of the V100's 300 W. This lower power draw translates to a suggested PSU requirement of 450 W, compared to 700 W for the V100. For dense server environments where power density and thermal management are critical constraints, the A10M is the more practical choice. Furthermore, the A10M offers 20 GB of GDDR6 memory, which is 4 GB more than the V100's 16 GB of HBM2. While the V100 has significantly higher memory bandwidth (897.0 GB/s vs. 500.2 GB/s), the A10M's larger capacity allows it to hold larger datasets, models, or working sets in memory without spilling to system RAM. This makes it a stronger contender for inference workloads that require large model footprints but are not as sensitive to memory bandwidth.

Architecture Differences

The architectural divide between these two GPUs is generational. The V100 is built on the Volta architecture using a 12 nm process at TSMC, featuring the GV100 chip. The A10M is built on the Ampere architecture using an 8 nm process at Samsung, featuring the GA102 chip. This process shrink allows the A10M to pack 28,300 million transistors into a smaller 628 mm² die, yielding a transistor density of 45.1M per mm², compared to the V100's 21,100 million transistors on a larger 815 mm² die (25.9M per mm²). Despite the smaller die, the A10M has more shading units (7,168 vs. 5,120), but fewer texture mapping units (224 vs. 320) and ROPs (80 vs. 128).

The most critical architectural feature difference is the inclusion of ray tracing cores. The A10M is equipped with 56 RT cores and 224 Tensor cores, while the V100 has no RT cores and 640 Tensor cores. The A10M also supports DirectX 12 Ultimate (12_2), whereas the V100 only supports DirectX 12 (12_1). This means the A10M is compliant with modern graphics API features like hardware-accelerated ray tracing and variable rate shading, making it a more versatile product for real-time rendering or hybrid compute-graphics workloads. The V100's Tensor cores are more numerous, but the A10M's architecture is newer. The memory subsystem also differs: the V100 uses 4096-bit HBM2, while the A10M uses a 320-bit GDDR6 interface. The V100's memory clock is 876 MHz (1752 Mbps effective), while the A10M's is 1563 MHz (12.5 Gbps effective), but the V100's wider bus gives it superior peak bandwidth.

FAQ

Q: Which GPU is faster in the Geekbench OpenCL benchmark?

A: The NVIDIA Tesla V100 PCIe 16 GB is significantly faster, scoring 163,063 points compared to the NVIDIA A10M's 135,230 points. This represents a 20.6% performance advantage for the V100 in this specific workload.

Q: Does the A10M have a power consumption advantage?

A: Yes, the A10M has a TDP of 150 W, which is half of the V100's 300 W. Consequently, the A10M requires a suggested PSU of 450 W, while the V100 suggests a 700 W PSU.

Q: Which GPU has more memory, and what is the interface?

A: The A10M has a larger memory pool of 20 GB using a 320-bit GDDR6 interface. The V100 has 16 GB using a 4096-bit HBM2 interface.

Q: Are there any architectural features unique to the A10M?

A: Yes, the A10M is based on the Ampere architecture and includes 56 RT cores for ray tracing, a feature absent from the Volta-based V100. It also supports DirectX 12 Ultimate, while the V100 supports DirectX 12 (12_1).

Q: How do the two GPUs compare in terms of memory bandwidth?

A: The V100 has significantly higher memory bandwidth at 897.0 GB/s due to its 4096-bit bus. The A10M, despite faster memory clocks, has a 320-bit bus and reaches only 500.2 GB/s.

Q: Which GPU has a higher FP32 throughput?

A: The A10M has a higher FP32 throughput, rated at 23.44 TFLOPS, compared to the V100's 14.13 TFLOPS. This is a notable contrast to the OpenCL benchmark results, indicating a workload-specific performance profile.

Specification Differences

The following table highlights the key specification differences between the two GPUs, focusing on fields where they diverge.

| Specification | NVIDIA Tesla V100 PCIe 16 GB | NVIDIA A10M |

| :--- | :--- | :--- |

| Architecture | Volta | Ampere |

| Process Node | 12 nm | 8 nm |

| Foundry | TSMC | Samsung |

| Transistors | 21,100 million | 28,300 million |

| Die Size | 815 mm² | 628 mm² |

| Transistor Density | 25.9M / mm² | 45.1M / mm² |

| Base Clock | 1245 MHz | 975 MHz |

| Boost Clock | 1380 MHz | 1635 MHz |

| Memory Size | 16 GB | 20 GB |

| Memory Type | HBM2 | GDDR6 |

| Memory Bus Width | 4096 bit | 320 bit |

| Memory Bandwidth | 897.0 GB/s | 500.2 GB/s |

| Shading Units | 5120 | 7168 |

| TMUs | 320 | 224 |

| ROPs | 128 | 80 |

| RT Cores | 0 | 56 |

| Tensor Cores | 640 | 224 |

| Pixel Rate | 176.6 GPixel/s | 130.8 GPixel/s |

| Texture Rate | 441.6 GTexel/s | 366.2 GTexel/s |

| FP32 Performance | 14.13 TFLOPS | 23.44 TFLOPS |

| FP16 Performance | 28.26 TFLOPS (2:1) | 23.44 TFLOPS (1:1) |

| TDP | 300 W | 150 W |

| Slot Width | Dual-slot | Single-slot |

| Power Connectors | 2x 8-pin | 8-pin EPS |

| Suggested PSU | 700 W | 450 W |

| Bus Interface | PCIe 3.0 x16 | PCIe 4.0 x16 |

| DirectX Support | 12 (12_1) | 12 Ultimate (12_2) |

| Length | Not specified | 267 mm (10.5 inches) |

| Height | Not specified | 112 mm (4.4 inches) |

Head-to-Head Benchmarks

The sole head-to-head benchmark available is the Geekbench OpenCL test, which provides a definitive result in favor of the Tesla V100. The V100 posted a score of 163,063 points, against the A10M's 135,230 points. This yields a delta of 20.6% in favor of the V100. This is a substantial margin that suggests the V100's memory bandwidth advantage and its Volta compute architecture provide a tangible benefit in this synthetic compute workload.

It is important to interpret this result in context. The V100's victory in OpenCL does not negate the A10M's architectural advantages. The A10M's FP32 throughput is notably higher (23.44 TFLOPS vs. 14.13 TFLOPS), yet it still loses the OpenCL test. This implies that the OpenCL workload is likely sensitive to memory bandwidth or specific compute paths where the V100 excels. The V100's 897.0 GB/s bandwidth is 79.3% higher than the A10M's 500.2 GB/s, which is likely the dominant factor in this test. For a workload that is less bandwidth-bound and more dependent on raw shader or FP32 compute, the A10M's higher clock speed (1635 MHz boost vs. 1380 MHz) and greater number of shading units could potentially narrow or reverse the gap, but the data does not support that conclusion.

The percentile data places both GPUs in the 96th percentile of all GPUs, indicating they are both high-end parts. The V100's average benchmark score of 138,063 is also higher than the A10M's 135,230, reinforcing the V100's overall performance edge in aggregated metrics. Looking at the nearest rivals, the V100 is a close competitor to the AMD Instinct MI100 (139,035, -0.7% delta) and the NVIDIA Tesla V100 SXM2 32 GB (137,731, 0.2% delta). The A10M, on the other hand, sits almost exactly level with the NVIDIA RTX 4000 Ada Generation (135,218, 0% delta) and is slightly behind the AMD Radeon PRO W6800 (135,396, -0.1% delta). These relationships place the A10M in a performance class that is measurably lower than the V100, at least in this single benchmark.