NVIDIA A2

The NVIDIA A2 is a workstation-oriented GPU built on the 8 nm Samsung process with the GA107 chip, featuring 8,700 million transistors on a 200 mm² die. It is part of the Workstation Ampere generation and is now end-of-life, with a release date of November 9, 2021. The card sits in the 79th percentile of all GPUs based on its average benchmark score of 34,866 in Geekbench OpenCL.

Power and Cooling

The NVIDIA A2 carries a thermal design power (TDP) of just 60 W, making it one of the most power-efficient workstation cards in its class. This low power envelope allows for a single-slot cooling solution, and the card does not require any external power connectors — it draws all its power directly from the PCIe slot. For system integration, a suggested power supply of 250 W is recommended, which is modest by current workstation standards. The absence of power connectors simplifies installation in dense server or multi-GPU configurations, where space and cabling are at a premium. The single-slot design further enhances its suitability for high-density compute environments, as multiple cards can be installed without the bulk of dual-slot coolers. The 60 W TDP also means that thermal management is less demanding than for higher-end workstation parts, allowing for simpler chassis airflow designs. This combination of low power draw and compact mechanical footprint positions the A2 as a low-impact compute accelerator rather than a performance-oriented graphics card.

Ray Tracing and Feature Set

The NVIDIA A2 is built on the Ampere architecture and includes dedicated ray tracing and tensor hardware. It features 10 RT cores and 40 tensor cores, enabling hardware-accelerated ray tracing and AI inference workloads. The GPU supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, covering the full modern API stack for professional and compute applications. The presence of tensor cores is particularly significant for machine learning inference tasks, where the A2 can accelerate matrix operations beyond what traditional shading units can achieve. The pixel rate is 56.64 GPixel/s and the texture rate is 70.80 GTexel/s, which are modest figures but adequate for its intended compute-focused role. The card has no display outputs, confirming that it is designed exclusively as a compute or rendering accelerator rather than a display adapter. This means all processing results must be read back via the PCIe 4.0 x8 bus interface, which offers sufficient bandwidth for most inference and rendering workloads. The 1:1 FP16 to FP32 ratio of 4.531 TFLOPS each indicates balanced half-precision and single-precision performance, a configuration that is beneficial for AI training where mixed-precision arithmetic is common.

Memory Subsystem

The A2 is equipped with 16 GB of GDDR6 memory on a 128-bit bus, yielding a memory bandwidth of 200.1 GB/s. The memory clock is 1563 MHz, with an effective data rate of 12.5 Gbps. This capacity is substantial for a 60 W card and is a key differentiator — the 16 GB frame buffer allows the A2 to handle large models and datasets that would exceed the memory capacity of smaller workstation cards. However, the 128-bit bus width limits memory bandwidth, and at 200.1 GB/s, the card may be constrained in memory-bound workloads that require rapid data streaming. For high-resolution rendering or large batch inference, the capacity is ample, but the bandwidth is a potential bottleneck when compared to cards with wider buses. The GDDR6 memory type is standard for this generation, offering a balance between speed and power efficiency. In practice, this means the A2 is best suited for workloads where memory capacity matters more than raw bandwidth — for example, running large neural network inference models that fit entirely within 16 GB but do not require extremely high throughput. The memory configuration is a deliberate trade-off: high capacity at moderate speed, aligning with the card’s low-power design philosophy.

How It Compares

AMD Radeon RX 9070: The nearest rival in average benchmark score is the AMD Radeon RX 9070, which scores 34,780 versus the A2’s 34,866. The A2 is 0.2% faster, a negligible margin that places the two cards at statistical parity in this specific workload. However, the RX 9070 is a consumer gaming card, while the A2 is a workstation compute accelerator — their architectural priorities differ significantly.

NVIDIA Quadro GV100: The Quadro GV100 scores 34,677, putting it 0.5% behind the A2. This is a notable comparison because the GV100 is a much larger and more power-hungry GPU from an earlier generation, yet the A2 matches its performance in this OpenCL test while consuming a fraction of the power. This underscores the efficiency gains of the Ampere architecture.

AMD Radeon HD 7970: The HD 7970, a legacy GPU from a much older generation, scores 34,541, trailing the A2 by 0.9%. The fact that a modern low-power workstation card edges out a high-end gaming card from its era highlights the substantial generational improvements in compute performance per watt.

AMD Radeon PRO W6400: The PRO W6400 scores 34,511, which is 1% behind the A2. Both are workstation-oriented cards, but the A2 offers 16 GB of memory versus the W6400’s more limited configuration, giving it a significant capacity advantage for large datasets despite the similar overall compute score.

Benchmark Performance

The Geekbench OpenCL score of 34,866 places the NVIDIA A2 in the 79th percentile of all GPUs, indicating that it outperforms the majority of graphics cards in this compute benchmark. The delta percentages against its nearest rivals are all within a narrow range: 0.2% ahead of the RX 9070, 0.5% ahead of the Quadro GV100, 0.9% ahead of the HD 7970, and 1% ahead of the PRO W6400. These margins are extremely tight, suggesting that the A2 is precisely positioned in a performance cluster where several very different cards deliver nearly identical OpenCL throughput. The practical implication is that in raw compute terms, the A2 is interchangeable with these rivals for many workloads, but its distinguishing features lie elsewhere — the 16 GB memory capacity, the 60 W power draw, and the single-slot form factor. The FP32 performance of 4.531 TFLOPS is consistent with this score, as is the 56.64 GPixel/s pixel rate. The data shows that the A2 is not a performance leader in absolute terms, but it achieves this level of compute with exceptional efficiency. The 1:1 FP16 ratio is particularly valuable for AI inference, where half-precision arithmetic is common, and the tensor cores provide additional acceleration for matrix operations. In a multi-GPU server configuration, the A2’s power efficiency means that many cards can be packed into a single chassis without exceeding thermal or power limits, potentially delivering aggregate performance that rivals fewer, larger GPUs. The benchmark results indicate that the A2 is a specialized compute accelerator that prioritizes capacity and efficiency over peak throughput, making it a sensible choice for specific inference or rendering tasks where its 16 GB memory and low power draw are decisive advantages.