NVIDIA A10G vs NVIDIA Tesla V100 PCIe 32 GB Comparison

NVIDIA A10G and NVIDIA Tesla V100 PCIe 32 GB represent two distinct eras of server acceleration, with the A10G built on the 8 nm Ampere architecture and the V100 on the 12 nm Volta architecture. Their average benchmark scores are nearly identical, with the A10G at 151,963 and the V100 at 150,305, a difference of just 1.1% in the A10G’s favor. However, the underlying specifications and workload characteristics diverge sharply, making the choice between them dependent on whether the task favors raw compute throughput or memory capacity and bandwidth.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA A10G has a slightly higher average benchmark score of 151,963, compared to the NVIDIA Tesla V100 PCIe 32 GB’s 150,305. The A10G leads by a margin of 1.1% according to the nearestRivals data.

Q: How do the two GPUs compare in OpenCL performance?

A: The NVIDIA Tesla V100 PCIe 32 GB wins the Geekbench OpenCL test with a score of 168,763, while the NVIDIA A10G scores 158,063. This gives the V100 a 6.3% advantage in that specific workload.

Q: Which GPU performs better in Vulkan benchmarks?

A: The NVIDIA A10G wins the Geekbench Vulkan test with a score of 145,863, against the NVIDIA Tesla V100 PCIe 32 GB’s 131,847. The A10G’s advantage here is 10.6%.

Q: What are the memory capacities of these two GPUs?

A: The NVIDIA A10G comes with 24 GB of GDDR6 memory, while the NVIDIA Tesla V100 PCIe 32 GB offers 32 GB of HBM2 memory. The V100 also has a larger 4096-bit memory bus width compared to the A10G’s 384-bit bus.

Q: Which GPU has a higher transistor count?

A: The NVIDIA A10G has 28,300 million transistors on a 628 mm² die, whereas the NVIDIA Tesla V100 PCIe 32 GB has 21,100 million transistors on a larger 815 mm² die. The A10G’s 8 nm process node allows for a much higher transistor density of 45.1M / mm² versus the V100’s 25.9M / mm².

Q: Are both GPUs still in production?

A: No, both the NVIDIA A10G and the NVIDIA Tesla V100 PCIe 32 GB are marked as end-of-life in the production status field. Their release dates were 2021-04-11 and 2018-03-26, respectively.

Architecture Differences

The architectural divide between the NVIDIA A10G and the NVIDIA Tesla V100 PCIe 32 GB is fundamental. The A10G is built on the Ampere architecture (chip GA102) using an 8 nm process from Samsung, while the V100 uses the older Volta architecture (chip GV100) on a 12 nm process from TSMC. This process difference is stark: the A10G packs 28,300 million transistors into a 628 mm² die, yielding a density of 45.1M / mm², whereas the V100’s 21,100 million transistors occupy a much larger 815 mm² die with a density of only 25.9M / mm².

The compute configurations are also radically different. The A10G has 9,216 shading units, 288 TMUs, and 96 ROPs, along with 72 dedicated ray tracing cores and 288 tensor cores. The V100 has fewer shading units (5,120), more TMUs (320), and more ROPs (128), but it lacks ray tracing cores entirely and instead fields 640 tensor cores. The A10G’s FP32 throughput is 31.52 TFLOPS, while the V100 delivers 14.13 TFLOPS in FP32; however, the V100’s FP16 rate is 28.26 TFLOPS (2:1), which is close to the A10G’s 31.52 TFLOPS (1:1) FP16 figure.

Memory architecture is another major split. The A10G uses 24 GB of GDDR6 on a 384-bit bus, with a bandwidth of 600.2 GB/s, while the V100 uses 32 GB of HBM2 on a 4096-bit bus, achieving 897.0 GB/s. The V100’s HBM2 memory offers 50% more capacity and nearly 50% higher bandwidth, which is a critical advantage for memory-bound workloads, despite the A10G’s newer process node and higher raw compute counts.

Head-to-Head Benchmarks

The head-to-head benchmark data shows a split decision between the two GPUs. In Geekbench OpenCL, the NVIDIA Tesla V100 PCIe 32 GB wins decisively with a score of 168,763 against the A10G’s 158,063, a delta of -6.3% for the A10G. This result suggests that the V100’s higher memory bandwidth (897.0 GB/s vs. 600.2 GB/s) and larger memory capacity (32 GB vs. 24 GB) provide a tangible benefit in OpenCL workloads, which often stress memory access patterns heavily.

Conversely, in Geekbench Vulkan, the NVIDIA A10G takes the lead with a score of 145,863, while the V100 trails at 131,847, giving the A10G a 10.6% advantage. This outcome aligns with the A10G’s superior shading unit count (9,216 vs. 5,120) and its doubled FP32 throughput (31.52 TFLOPS vs. 14.13 TFLOPS), which likely drive better performance in graphics-oriented or compute-heavy Vulkan tasks. The A10G also benefits from newer API support, including DirectX 12 Ultimate (12_2) versus the V100’s DirectX 12 (12_1).

When looking at the overall average benchmark scores, the A10G’s 151,963 edges out the V100’s 150,305 by 1.1%, but this narrow margin masks the divergent strengths. The V100’s OpenCL win is substantial, but the A10G’s Vulkan win is even larger in percentage terms, which is reflected in the A10G’s slightly higher average. The nearestRivals data further contextualizes these scores: the A10G is 9.3% ahead of the AMD Instinct MI100 (139,035) but 5.4% behind the AMD Radeon Pro W6800X (160,671) and 6.5% behind the NVIDIA A100 PCIe 40 GB (162,504). The V100 similarly trails the W6800X by 6.5% and the A100 by 7.5%, while leading the MI100 by 8.1%.

The Verdict

The data points to a clear split: the NVIDIA Tesla V100 PCIe 32 GB is the choice for workloads that prioritize memory capacity and bandwidth, as evidenced by its OpenCL dominance. With 32 GB of HBM2 and 897.0 GB/s of bandwidth, it outperforms the A10G by 6.3% in OpenCL, making it suitable for large datasets that must fit in GPU memory. The V100’s 640 tensor cores also give it a strong FP16 compute rate of 28.26 TFLOPS, which is close to the A10G’s 31.52 TFLOPS, despite the V100’s older architecture.

Conversely, the NVIDIA A10G is better suited for compute-bound tasks that leverage its massive FP32 throughput and modern feature set. Its 31.52 TFLOPS FP32 is more than double the V100’s 14.13 TFLOPS, and its 10.6% Vulkan win demonstrates clear superiority in that API. The A10G’s 72 ray tracing cores and DirectX 12 Ultimate support also make it more future-proof for graphics-adjacent workloads, even in a server context. For users who need raw compute density in a single-slot form factor with lower power draw (150 W vs. 250 W), the A10G is the stronger option.

Specification Differences

The specification differences between the two GPUs are extensive and directly influence their benchmark outcomes. The NVIDIA A10G uses a GA102 chip on an 8 nm Samsung process, while the NVIDIA Tesla V100 PCIe 32 GB uses a GV100 chip on a 12 nm TSMC process. The A10G has a base clock of 1320 MHz and a boost clock of 1710 MHz, compared to the V100’s 1230 MHz base and 1380 MHz boost. Memory clocks also differ: the A10G runs at 1563 MHz (12.5 Gbps effective), while the V100 runs at 876 MHz (1752 Mbps effective).

Memory specifications favor the V100 heavily: it has 32 GB of HBM2 with a 4096-bit bus and 897.0 GB/s bandwidth, versus the A10G’s 24 GB of GDDR6 with a 384-bit bus and 600.2 GB/s bandwidth. The compute units are in the A10G’s favor for shading (9,216 vs. 5,120) and texture mapping (288 vs. 320 TMUs, with the V100 slightly ahead), but the V100 has more ROPs (128 vs. 96) and more tensor cores (640 vs. 288). The A10G also features 72 ray tracing cores, which the V100 lacks entirely.

Power and physical specifications also differ: the A10G has a TDP of 150 W with a single-slot design and an 8-pin EPS connector, requiring a 450 W suggested PSU, while the V100 has a 250 W TDP, a dual-slot design, 2x 8-pin connectors, and a 600 W suggested PSU. The A10G is 267 mm long and 112 mm high, whereas the V100’s dimensions are not listed. Both use PCIe interfaces, but the A10G uses PCIe 4.0 x16 while the V100 uses PCIe 3.0 x16. Both have no display outputs and support OpenGL 4.6 and Vulkan 1.4, but the A10G supports DirectX 12 Ultimate (12_2) versus the V100’s DirectX 12 (12_1).

Where Each One Wins

The NVIDIA Tesla V100 PCIe 32 GB wins in scenarios that depend on memory bandwidth and capacity. Its 897.0 GB/s bandwidth and 32 GB capacity give it a 6.3% edge in OpenCL benchmarks, making it the better pick for large-scale data processing, scientific simulations, or any workload where memory size is the bottleneck. The V100’s higher ROP count (128 vs. 96) and TMU count (320 vs. 288) also suggest an advantage in certain rasterization or texture-heavy tasks, even though its overall shading power is lower.

The NVIDIA A10G wins in compute-heavy and graphics-oriented workloads. Its 10.6% Vulkan advantage is backed by 9,216 shading units, 72 ray tracing cores, and 31.52 TFLOPS FP32 performance, which is 2.2 times the V100’s FP32 rate. The A10G’s 288 tensor cores, while fewer than the V100’s 640, still deliver competitive FP16 performance (31.52 TFLOPS vs. 28.26 TFLOPS) due to the 1:1 ratio. For tasks that are not memory-limited, the A10G’s higher clock speeds (1710 MHz boost vs. 1380 MHz) and newer architecture provide a clear performance edge. Additionally, the A10G’s single-slot design and lower TDP (150 W vs. 250 W) make it more efficient for dense server deployments, though the V100’s dual-slot design may offer better thermal headroom for sustained loads.