NVIDIA A10G vs NVIDIA Tesla V100 PCIe 16 GB Comparison

# NVIDIA A10G vs NVIDIA Tesla V100 PCIe 16 GB

The NVIDIA A10G and NVIDIA Tesla V100 PCIe 16 GB represent two distinct generations of server accelerators, with the data showing a split decision: the A10G wins the Vulkan benchmark by a substantial margin, while the V100 takes the OpenCL benchmark by a smaller amount. The A10G carries a 97th percentile ranking among all GPUs, while the V100 sits at the 96th percentile, indicating both are high-performing parts despite their architectural differences.

Where Each One Wins

The benchmark results reveal a clear use-case split between the two cards. The NVIDIA A10G dominates in the Geekbench Vulkan test, scoring 145,863 against the V100’s 113,062, a 29% advantage. This suggests the A10G is the stronger choice for workloads that leverage Vulkan’s modern graphics and compute API, which typically benefits from newer hardware features and higher raw shading throughput.

Conversely, the NVIDIA Tesla V100 PCIe 16 GB wins the Geekbench OpenCL test, scoring 163,063 versus the A10G’s 158,063, a 3.1% edge. OpenCL remains widely used in scientific computing and legacy compute stacks, and the V100’s victory here indicates that its architecture—with a 4096-bit memory bus and HBM2—still holds its own in memory-intensive OpenCL workloads despite being several years older.

Looking at the average benchmark scores, the A10G posts 151,963 while the V100 averages 138,063, giving the A10G a roughly 10% overall advantage. This aligns with the A10G’s higher percentile ranking (97th vs 96th) and suggests that for mixed or unknown workloads, the A10G is the safer bet. However, the V100’s OpenCL win is not trivial—it signals that specific software stacks may still favor the older card.

The nearest-rival data reinforces this split. The A10G’s closest competitor is the Tesla V100 PCIe 32 GB at an average score of 150,305, just 1.1% behind, while the V100 16 GB’s nearest rival is the V100 SXM2 32 GB at 137,731, only 0.2% ahead. Both cards are tightly grouped with their peers, but the A10G’s rivals are generally faster, indicating it competes in a higher performance tier.

Architecture Differences

The architectural gap between these two GPUs is substantial. The A10G is built on the GA102 chip using Samsung’s 8 nm process, while the V100 uses the GV100 chip on TSMC’s 12 nm node. The A10G packs 28,300 million transistors into a 628 mm² die, whereas the V100 fits 21,100 million transistors into a larger 815 mm² die. This results in a transistor density of 45.1 million per square millimeter for the A10G versus 25.9 million for the V100—a direct consequence of the newer manufacturing process.

The A10G belongs to the Ampere architecture (Server Ampere generation), featuring 9,216 shading units, 288 texture mapping units, 96 raster output units, 72 ray tracing cores, and 288 tensor cores. The V100, from the Volta architecture (Tesla Volta generation), has 5,120 shading units, 320 TMUs, 128 ROPs, and 640 tensor cores—but notably lacks ray tracing cores entirely. This means the A10G supports hardware-accelerated ray tracing, while the V100 does not.

Clock speeds also differ markedly. The A10G runs at a 1320 MHz base and 1710 MHz boost, while the V100 operates at 1245 MHz base and 1380 MHz boost. This higher boost clock, combined with more than double the shading units, gives the A10G a massive FP32 throughput advantage: 31.52 TFLOPS versus 14.13 TFLOPS. The FP16 story is more nuanced: the A10G delivers 31.52 TFLOPS at a 1:1 ratio, while the V100 achieves 28.26 TFLOPS at a 2:1 ratio, meaning the V100’s FP16 performance is halved relative to its FP32 rate.

Memory architecture is another major differentiator. The A10G uses 24 GB of GDDR6 on a 384-bit bus, yielding 600.2 GB/s of bandwidth. The V100 uses 16 GB of HBM2 on a 4096-bit bus, delivering 897.0 GB/s—nearly 50% more bandwidth despite less capacity. The V100’s memory clocks at 876 MHz (1752 Mbps effective), while the A10G’s memory runs at 1563 MHz (12.5 Gbps effective). This explains why the V100 can still win memory-bound OpenCL workloads despite its older architecture.

Head-to-Head Benchmarks

The two benchmark tests tell a compelling story. In Geekbench OpenCL, the NVIDIA Tesla V100 PCIe 16 GB scores 163,063 against the A10G’s 158,063, a delta of -3.1% for the A10G. This is a modest but real victory for the V100, likely driven by its superior memory bandwidth (897.0 GB/s vs 600.2 GB/s) and larger memory bus (4096-bit vs 384-bit). OpenCL workloads often scale with memory throughput, and the V100’s HBM2 advantage appears to overcome its lower compute throughput in this specific test.

The Geekbench Vulkan result flips the script dramatically. The A10G scores 145,863, while the V100 manages only 113,062—a 29% delta in favor of the A10G. This is a dominant win, reflecting the A10G’s newer architecture, higher shading unit count (9,216 vs 5,120), and support for DirectX 12 Ultimate (12_2) compared to the V100’s DirectX 12 (12_1). Vulkan’s low-level nature tends to favor newer hardware with better driver optimization and more parallel execution units, and the A10G delivers on both fronts.

The wins are split exactly evenly: one test each. However, the magnitude matters. The A10G’s 29% Vulkan victory dwarfs the V100’s 3.1% OpenCL margin. If a workload uses Vulkan, the A10G is overwhelmingly faster; if it uses OpenCL, the V100 is only slightly ahead. For users deciding between the two, the API and software stack they rely on will dictate the winner.

FAQ

Q: Which GPU has higher raw FP32 compute performance?

A: The NVIDIA A10G delivers 31.52 TFLOPS FP32, while the Tesla V100 PCIe 16 GB offers 14.13 TFLOPS, making the A10G over twice as fast in single-precision floating-point workloads.

Q: Why does the V100 win the OpenCL benchmark despite lower compute specs?

A: The V100 has significantly higher memory bandwidth at 897.0 GB/s compared to the A10G’s 600.2 GB/s, and its 4096-bit HBM2 bus likely provides an advantage in memory-bound OpenCL tasks.

Q: Does the A10G support hardware ray tracing?

A: Yes, the A10G includes 72 ray tracing cores as part of its Ampere architecture, while the V100 has no RT cores listed in its specifications.

Q: What are the memory capacity and type differences?

A: The A10G has 24 GB of GDDR6 memory, while the V100 has 16 GB of HBM2. The V100’s total bandwidth is higher, but the A10G offers 50% more capacity.

Q: How do their average benchmark scores compare?

A: The A10G averages 151,963 across its benchmarks, while the V100 averages 138,063—a difference of roughly 10% in favor of the A10G overall.

Q: Which card has better API support?

A: The A10G supports DirectX 12 Ultimate (12_2), while the V100 supports DirectX 12 (12_1). Both support OpenGL 4.6 and Vulkan 1.4.

The Verdict

The data paints a clear picture: choose the NVIDIA A10G if your workloads use Vulkan or require modern feature support like ray tracing, higher FP32 throughput, or more memory capacity. Its 29% Vulkan win, 31.52 TFLOPS FP32, and 24 GB of GDDR6 make it the superior all-rounder for contemporary compute tasks. The A10G also carries a higher average benchmark score (151,963 vs 138,063) and a better percentile ranking (97th vs 96th), reinforcing its overall performance edge.

Choose the NVIDIA Tesla V100 PCIe 16 GB if your software stack is built around OpenCL and depends heavily on memory bandwidth. The V100’s 897.0 GB/s bandwidth and 4096-bit bus give it a genuine advantage in memory-bound scenarios, as evidenced by its 3.1% OpenCL win. Its lower power draw of 300 W (still higher than the A10G’s 150 W) and dual-slot form factor may also be acceptable trade-offs for legacy deployments.

For most users, the A10G is the better investment. Its wins are larger, its architecture is newer, and its specs are superior in nearly every compute category. The V100’s lone OpenCL victory is real but narrow, and it comes with significant compromises: fewer shading units, no ray tracing, lower FP32, and less memory. The data suggests the A10G is the more future-proof choice, while the V100 remains a niche option for specific memory-hungry OpenCL applications.

Specification Differences

| Specification | NVIDIA A10G | NVIDIA Tesla V100 PCIe 16 GB |

|---|---|---|

| Chip | GA102 | GV100 |

| Architecture | Ampere | Volta |

| Process Node | 8 nm (Samsung) | 12 nm (TSMC) |

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

| Die Size | 628 mm² | 815 mm² |

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

| Base Clock | 1320 MHz | 1245 MHz |

| Boost Clock | 1710 MHz | 1380 MHz |

| Memory Size | 24 GB GDDR6 | 16 GB HBM2 |

| Memory Bus Width | 384 bit | 4096 bit |

| Memory Bandwidth | 600.2 GB/s | 897.0 GB/s |

| Shading Units | 9216 | 5120 |

| TMUs | 288 | 320 |

| ROPs | 96 | 128 |

| RT Cores | 72 | None |

| Tensor Cores | 288 | 640 |

| FP32 Performance | 31.52 TFLOPS | 14.13 TFLOPS |

| FP16 Performance | 31.52 TFLOPS (1:1) | 28.26 TFLOPS (2:1) |

| TDP | 150 W | 300 W |

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

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

| Suggested PSU | 450 W | 700 W |

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

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