NVIDIA A2 PCIe

The NVIDIA A2 PCIe is a low-power server accelerator built on the Ampere architecture. It combines a 60 W TDP, a single-slot design, and 16 GB of GDDR6 memory in a 168 mm package, targeting compute environments where space and power are at a premium. As an end-of-life product, it represents a specific niche in NVIDIA's server lineup, positioned between the preceding Tesla Turing generation and the subsequent Server Ada line. With no display outputs and a PCIe 4.0 x8 interface, it is purely a compute-oriented card, and its 50th percentile ranking among all GPUs in the database places it at the median of the performance distribution.

Who Should Consider It

The A2 PCIe is best suited for deployments that prioritize low power draw and compact physical footprint over raw compute throughput. Its 60 W TDP means it can be powered entirely through the PCIe slot, as indicated by the absence of power connectors, and the suggested PSU rating of 250 W is modest for a server chassis. The card’s 168 mm length and single-slot width allow it to fit into dense, space-constrained systems where larger accelerators would not be viable. For workloads that require moderate FP32 compute—rated at 4.531 TFLOPS—and up to 16 GB of memory, this card offers a balanced capacity-to-power ratio.

Given its 50th percentile placement, the A2 sits at the midpoint of all GPUs tracked in the database, meaning it is neither a high-end compute monster nor a negligible performer. In practical terms, it is appropriate for inference tasks on small to medium batch sizes, where the 16 GB frame buffer can hold model weights and intermediate activations without spilling to system memory. The 128-bit memory bus and 200.1 GB/s bandwidth are sufficient for many deep learning inference scenarios, but they become a bottleneck for training large models or processing high-resolution data. For edge or branch-office servers that handle occasional AI inference, the A2’s low power envelope and lack of external power cables simplify deployment. Conversely, it is not intended for heavy 3D rendering or high-end simulation, as the shading unit count (1280) and texture rate (70.80 GTexel/s) are modest, and the absence of display outputs eliminates any direct video output capability.

Ray Tracing and Feature Set

The A2 PCIe includes 10 RT cores and 40 tensor cores, bringing Ampere’s ray tracing and AI acceleration features to a low-power segment. The RT cores enable hardware-accelerated ray tracing for compute workloads that rely on path tracing or light transport simulation, though the low core count suggests that complex ray-traced scenes would execute slowly. The tensor cores are more significant: they provide dedicated matrix math for AI inference and training, which is the primary intended use case for this card. With 40 tensor cores, the A2 can accelerate FP16 operations at the same rate as FP32 (4.531 TFLOPS, 1:1 ratio), indicating that mixed-precision AI workloads will not see a throughput advantage over FP32, but the tensor cores themselves offer higher efficiency for matrix operations.

In terms of API support, the card is compliant with DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. This means that any compute or ray tracing code written against these APIs will run on the A2, though the lack of display outputs makes it a pure compute device—there is no frame presentation path. The DirectX 12 Ultimate feature level 12_2 ensures support for hardware ray tracing, mesh shaders, and variable-rate shading, but these features are of limited relevance in a server context. The Vulkan 1.4 support is notable for cross-platform compute workloads. The absence of any display connectors means that all output must be handled through network or other system interfaces, which is typical for server accelerators. The 10 RT cores are a token presence compared to dedicated RT-focused GPUs, but they do allow experimentation with ray-traced inference or scientific visualization without requiring a separate graphics card.

Benchmark Performance

The database lists no aggregate benchmark score for the A2 PCIe, and its average benchmark score is reported as 0, indicating that no standardized tests have been recorded for this part. Consequently, performance analysis must rely on the theoretical specifications provided. The FP32 compute throughput of 4.531 TFLOPS is a hard ceiling for general-purpose single-precision workloads. At that rate, the A2 would be roughly comparable to a mid-range consumer GPU from several generations ago, but in a server context it is positioned at the low end. The pixel rate of 56.64 GPixel/s and texture rate of 70.80 GTexel/s are derived from the 32 ROPs and 40 TMUs, respectively, and they reflect the card’s limited rasterization capabilities—again, not a focus for a compute accelerator.

The 50th percentile ranking among all GPUs in the database provides a relative anchor. This percentile is based on the full set of GPUs tracked, including consumer, workstation, and server parts, so the A2 sits exactly in the middle of that distribution. That means half of all GPUs in the database are slower, and half are faster, but the distribution includes many low-end integrated graphics and older parts, so the A2’s absolute performance is still modest. Without benchmark scores, it is impossible to state specific deltas against rivals, but the theoretical numbers suggest that the A2 would outperform most integrated graphics solutions while lagging dedicated gaming GPUs from the same era. The 60 W power limit is a key constraint; the card cannot boost beyond 1770 MHz for sustained periods without thermal or power throttling, and the base clock of 1440 MHz is likely to be the long-term average under load. The 8 nm process from Samsung, with 8,700 million transistors on a 200 mm² die, yields a transistor density of 43.5M per mm², which is moderate for the architecture.

How It Compares

The database does not list any nearest rivals for the A2 PCIe, so a direct comparison against specific competing products is not possible from the provided data. However, the card’s position in the product stack can be understood through its generational context. It succeeds the Tesla Turing line, which would have been based on the older Turing architecture, and it is succeeded by the Server Ada generation. The A2 is a member of the Server Ampere (Axx) family, and its 50th percentile ranking suggests it occupies a middle ground within that family—not the entry-level, but also not the high-end. The 60 W TDP and single-slot design are distinctive; many other server accelerators require more power and larger cooling solutions. The A2’s lack of display outputs is typical for compute-only cards, but its 16 GB memory capacity is generous for a 60 W part, which might give it an advantage in memory-bound inference tasks over lower-capacity rivals.

Without specific rival data, we can only note that the A2’s specifications are consistent with a low-power inference accelerator. Its FP32 throughput is lower than that of full-size Ampere server GPUs, but its power draw is a fraction of theirs. The 128-bit memory bus and 200.1 GB/s bandwidth are limiting factors for any workload that requires high memory throughput, such as large language model inference with long context windows. In contrast, tasks that are more compute-bound than memory-bound, such as small batch image classification, would be less affected by the narrow bus. The 10 RT cores and 40 tensor cores are present, but they are not numerous enough to compete with higher-end accelerators that have hundreds of tensor cores. Overall, the A2 is a niche product, and its end-of-life status means it is now legacy hardware, but it still offers a low-power entry point for Ampere-based compute.

Memory Subsystem

The A2 PCIe is equipped with 16 GB of GDDR6 memory, connected via a 128-bit bus. The memory clock is 1563 MHz, which translates to a 12.5 Gbps effective data rate, yielding a total bandwidth of 200.1 GB/s. This configuration is notable for its capacity-to-bandwidth ratio: 16 GB is a substantial amount of memory for a 60 W card, but the bandwidth is limited by the narrow bus. For high-resolution compute tasks, such as processing 4K or 8K images in medical imaging or remote sensing, the 16 GB capacity allows storing large batches of data, but the 200.1 GB/s bandwidth may become a bottleneck when data must be read or written frequently. In deep learning inference, the memory capacity is often more important than raw bandwidth for holding model weights, but the bandwidth affects throughput when processing many samples per second.

The 128-bit bus width is half of what many mainstream consumer GPUs use, and the effective bandwidth is lower than that of even entry-level gaming cards from the same era. However, the A2’s target workloads—typically small to medium batch inference—do not always saturate memory bandwidth. The 16 GB capacity is particularly useful for running models that exceed the 8 GB or 12 GB limits of smaller accelerators, allowing larger batch sizes or more complex models without swapping to system memory. The memory subsystem is also power-efficient: GDDR6 at 12.5 Gbps consumes less power per bit than higher-speed memory, aligning with the card’s low TDP. The lack of error-correcting code (ECC) is not mentioned in the fact pack, but the memory type is standard GDDR6, which is not typically ECC. For mission-critical compute, the absence of ECC could be a concern, but it is not documented here. Overall, the memory subsystem provides a balanced profile for a low-power server card, with generous capacity but modest bandwidth that may limit performance in memory-intensive scenarios.