NVIDIA A2 vs NVIDIA Quadro GV100 Comparison

The NVIDIA Quadro GV100 and NVIDIA A2 represent two very different approaches to professional computing, separated by three years of architectural evolution. The GV100 is a massive, power-hungry Volta-era flagship built for maximum compute throughput, while the A2 is a compact, efficient Ampere-based card designed for density and low power draw. Benchmark data shows the GV100 dominating in raw performance, but the A2’s modern feature set and drastically lower power requirements tell a more nuanced story for specific deployment scenarios.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA Quadro GV100 has a significantly higher average benchmark score of 35,520 compared to the NVIDIA A2’s 34,690. This places the GV100 2.4% ahead of the A2, according to the nearestRivals deltaPct data.

Q: How do the two cards compare in Geekbench OpenCL performance?

A: The Quadro GV100 scores 150,004 in Geekbench OpenCL, while the A2 scores 35,357. This gives the GV100 a massive 324.3% lead, making it over four times faster in this compute-oriented test.

Q: What is the difference in power consumption?

A: The Quadro GV100 has a TDP of 250 W and requires a 600 W suggested PSU, whereas the A2 has a TDP of 60 W and a suggested PSU of 250 W. The A2 also draws power solely from its PCIe slot, as it has no power connectors.

Q: Do both cards support ray tracing?

A: No. The Quadro GV100 has no RT cores listed, while the NVIDIA A2 includes 10 RT cores. This makes the A2 the only one of the two with dedicated hardware for ray-traced workloads.

Q: Which card has more memory bandwidth?

A: The Quadro GV100 features 32 GB of HBM2 memory on a 4096-bit bus, delivering 868.4 GB/s of bandwidth. The A2 offers 16 GB of GDDR6 on a 128-bit bus, providing 200.1 GB/s, which is substantially lower.

Q: Are both GPUs still in production?

A: No, both are end-of-life products. The Quadro GV100 was released in March 2018, while the A2 launched later in November 2021.

Architecture Differences

The architectural gap between these two GPUs is profound. The Quadro GV100 is built on TSMC’s 12 nm process and packs 21,100 million transistors onto a massive 815 mm² die. Its transistor density is 25.9 million per mm². In contrast, the A2 uses Samsung’s 8 nm node, integrates 8,700 million transistors into a compact 200 mm² die, and achieves a much higher density of 43.5 million per mm². This means the A2 is far more efficient in terms of transistors per area, reflecting the newer manufacturing technology.

The compute cores tell a story of scale versus efficiency. The GV100 houses 5,120 shading units, 320 TMUs, and 128 ROPs, alongside 640 Tensor Cores. The A2, by comparison, has only 1,280 shading units, 40 TMUs, and 32 ROPs, with 40 Tensor Cores. However, the A2 includes 10 RT cores, a feature entirely absent from the older Volta chip. The GV100’s FP32 throughput is 16.66 TFLOPS, while the A2 manages 4.531 TFLOPS, a 3.7x difference. Interestingly, the GV100’s FP16 performance is 33.32 TFLOPS (2:1 ratio), whereas the A2 offers 4.531 TFLOPS with a 1:1 ratio, showing that the older card was specifically optimized for mixed-precision workloads.

Memory architecture diverges completely. The GV100 uses HBM2 with a 4096-bit bus and 868.4 GB/s bandwidth, while the A2 uses GDDR6 with a 128-bit bus and 200.1 GB/s. The A2’s memory operates at 12.5 Gbps effective, and the GV100’s at 1696 Mbps effective. The GV100’s 32 GB capacity doubles the A2’s 16 GB, but the A2’s smaller memory footprint is clearly designed for lower power and cost efficiency. The API support also differs, with the A2 supporting DirectX 12 Ultimate (12_2) and the GV100 limited to DirectX 12 (12_1).

Head-to-Head Benchmarks

The available head-to-head data is limited to two Geekbench tests, and the results are decisively lopsided. In Geekbench OpenCL, the Quadro GV100 scores 150,004 against the A2’s 35,357. This 324.3% delta means the GV100 delivers more than four times the raw compute performance in this workload. The margin is so large that it suggests fundamental differences in compute capability, pointing directly to the GV100’s 5,120 shading units and 640 Tensor Cores versus the A2’s 1,280 and 40, respectively.

Geekbench Vulkan shows a similar trend. The GV100 posts 139,526, while the A2 scores 34,023, a 310.1% advantage for the older card. Despite the A2 having a newer architecture with RT cores, the sheer number of execution units in the GV100 overwhelms the A2 in these general-purpose compute tests. The A2’s higher clock speeds (1440 MHz base, 1770 MHz boost) versus the GV100’s (1132 MHz base, 1627 MHz boost) cannot compensate for the 4x difference in core count.

The wins tally stands at 2 for the GV100 and 0 for the A2 in these shared benchmarks. However, it is critical to note that the A2 lacks any Passmark results in the data, while the GV100 has scores for DirectX 10, 11, 12, and 9, plus G2D and G3D tests. This absence means the A2’s performance in legacy graphics APIs is unmeasured, leaving a gap in the comparative picture. The GV100’s Passmark G3D score of 19,650 and compute score of 9,069 highlight its strength in traditional 3D rendering and compute, but no equivalent data exists for the A2.

Specification Differences

The two cards differ in nearly every specification category. The process node shifts from 12 nm (TSMC) to 8 nm (Samsung), and transistor count drops from 21,100 million to 8,700 million. Die size shrinks dramatically from 815 mm² to 200 mm², while transistor density improves from 25.9M/mm² to 43.5M/mm².

Clock speeds favor the A2, which has a 1440 MHz base and 1770 MHz boost, compared to the GV100’s 1132 MHz base and 1627 MHz boost. Memory type, capacity, bus width, and bandwidth all favor the GV100, which uses HBM2 with 32 GB, 4096-bit bus, and 868.4 GB/s, versus the A2’s GDDR6 with 16 GB, 128-bit bus, and 200.1 GB/s. The effective memory clock is 1696 Mbps for the GV100 and 12.5 Gbps for the A2.

Core counts heavily favor the GV100: 5,120 shading units versus 1,280, 320 TMUs versus 40, and 128 ROPs versus 32. The GV100 also has 640 Tensor Cores to the A2’s 40, but the A2 counters with 10 RT cores where the GV100 has none. Pixel rate is 208.3 GPixel/s for the GV100 versus 56.64 GPixel/s for the A2, and texture rate is 520.6 GTexel/s versus 70.80 GTexel/s. FP32 is 16.66 TFLOPS versus 4.531 TFLOPS, and FP16 is 33.32 TFLOPS versus 4.531 TFLOPS.

Power requirements diverge sharply. The GV100’s TDP is 250 W, needing a 600 W PSU and a single 8-pin connector. The A2’s TDP is just 60 W, requires no power connectors, and works with a 250 W PSU. The GV100 is dual-slot with 4x DisplayPort 1.4a outputs, while the A2 is single-slot with no display outputs. The GV100 uses PCIe 3.0 x16, and the A2 uses PCIe 4.0 x8. DirectX support differs, with the A2 offering 12 Ultimate (12_2) versus the GV100’s 12 (12_1). The GV100 has a launch MSRP of 8,999 USD, while the A2 has no listed MSRP.

Where Each One Wins

The Quadro GV100 is the clear winner in raw compute performance. Its 324.3% advantage in OpenCL and 310.1% lead in Vulkan make it the superior choice for compute-heavy tasks like scientific simulation, deep learning inference, and high-performance rendering. The 32 GB of HBM2 memory with 868.4 GB/s bandwidth is ideal for large datasets that must reside in GPU memory, and the 640 Tensor Cores provide substantial acceleration for AI workloads. The dual-slot design with DisplayPort outputs also makes it suitable for traditional workstation visualization with multiple monitors.

The NVIDIA A2 wins on power efficiency and physical footprint. At 60 W TDP with no power connectors, it can be deployed in servers without additional power cabling, and its single-slot form factor allows for high-density installations. The RT cores enable hardware-accelerated ray tracing, which the GV100 cannot do. The PCIe 4.0 x8 interface offers higher per-lane bandwidth than the GV100’s PCIe 3.0 x16, which could benefit certain data-transfer-bound workloads. The A2’s support for DirectX 12 Ultimate also makes it more future-proof for modern graphics APIs.

The Verdict

The data supports a straightforward split decision. For users prioritizing maximum compute throughput and large memory capacity, the Quadro GV100 is the obvious choice. Its benchmark scores are over four times higher than the A2 in both OpenCL and Vulkan, and its 32 GB HBM2 pool is unmatched by the A2’s 16 GB GDDR6. The GV100’s 80th percentile ranking among all GPUs, versus the A2’s 79th, reinforces its slight edge in overall performance.

However, the A2 is the better pick for environments where power and space are constrained. Its 60 W TDP is a fraction of the GV100’s 250 W, and the absence of power connectors simplifies installation. The inclusion of RT cores makes it the only option for ray-traced workloads, and its PCIe 4.0 interface and DirectX 12 Ultimate support are modern advantages. The A2’s nearest rivals include the NVIDIA T1000 8 GB and RTX A1000, against which it holds a 0.4% and 1.4% lead, respectively, showing it remains competitive within its efficiency class.

Ultimately, the choice hinges on workload priorities. The GV100 is a performance behemoth for compute and visualization tasks where power is not a limiting factor. The A2 is a specialized accelerator for dense, low-power deployments that require modern features like ray tracing. The benchmark data leaves no ambiguity: the GV100 wins on raw speed, but the A2 wins on efficiency and architectural modernity.