NVIDIA GRID A100A

Power and Cooling

The NVIDIA GRID A100A is a 400 W part, placing it firmly in the high-power segment of the professional accelerator market. This power envelope is substantial, and the platform guidance reflects that reality: the suggested PSU rating is 800 W, which provides a reasonable overhead for a system built around this accelerator. The board is designed as an IGP (integrated graphics processor) solution, meaning it is not a typical add-in card that slots into a standard expansion slot. This form factor inherently shapes its cooling and power delivery requirements, as it is intended for dense, purpose-built server chassis rather than consumer desktops.

Notably, the card requires no external power connectors. This is a critical design detail driven by its IGP nature; power is delivered through the host system's backplane or proprietary board design, not through standard 8-pin or 12-pin PCIe power cables. For a system integrator, this simplifies cabling but also mandates a motherboard or carrier board designed specifically to supply 400 W to the accelerator. The absence of discrete power connectors also implies that thermal management is handled by the chassis-level cooling solution, not by the card's own fans or heatsink assembly. The data indicates a 7 nm TSMC process node and a massive 54,200 million transistor count on an 826 mm² die, which contributes to the high power draw but also enables the compute density that this accelerator is known for. In practical terms, the 400 W TDP means that any system housing this card must have robust airflow and heat extraction capabilities, especially in multi-GPU configurations where several such accelerators operate simultaneously.

Memory Subsystem

The memory subsystem is a defining characteristic of the GRID A100A, built around a 32 GB HBM2e configuration. This is not a consumer-grade memory setup; it leverages a 6144-bit memory bus, which is extraordinarily wide. The effective memory clock is listed at 2.4 Gbps, but when paired with that immense bus width, the resulting bandwidth is a staggering 1.87 TB/s. For context, this level of bandwidth is essential for workloads that saturate memory, such as large language model inference, scientific simulations, and high-resolution rendering. A narrower bus with higher clock speeds cannot achieve the same aggregate throughput because bandwidth is the product of bus width and clock rate; here, the width dominates.

For high-resolution applications, the implications are clear. At 4K and beyond, frame buffers and texture datasets grow exponentially, and a 32 GB pool is sufficient to hold substantial scenes or model weights entirely in VRAM, avoiding costly spills to system memory. The 1.87 TB/s bandwidth ensures that the GPU's compute units are fed with data at a rate that prevents stalling, even under heavy multi-stream workloads. The pixel rate of 270.7 GPixel/s and texture rate of 609.1 GTexel/s further indicate that the memory subsystem is not a bottleneck; these rates are only achievable if the memory can deliver data fast enough. In summary, the HBM2e implementation here is designed for capacity and bandwidth in equal measure, making the card suitable for tasks that would otherwise be constrained by memory throughput on conventional GDDR6 solutions.

Ray Tracing and Feature Set

The GRID A100A is built on the Ampere architecture, using the GA100 chip. However, the data shows that the card does not ship with dedicated ray tracing cores; the rtCores field is null. This is a critical distinction from consumer Ampere GPUs, which include RT cores for hardware-accelerated ray tracing. Instead, this accelerator focuses on compute and tensor operations. The tensor core count is listed at 432, which is a significant number and indicates a strong focus on AI and machine learning workloads rather than real-time graphics ray tracing. The FP16 performance is 77.97 TFLOPS at a 4:1 ratio, which is exactly the kind of metric that matters for deep learning training and inference, where mixed-precision arithmetic is common. The FP32 performance stands at 19.49 TFLOPS, which is more relevant for traditional HPC and simulation tasks.

The API support fields for DirectX, OpenGL, and Vulkan are all null, which reinforces the notion that this is not a graphics-first product. The display outputs are listed as "No outputs," meaning this card is not designed to drive monitors. It is a pure compute accelerator for server environments. The feature set, therefore, centers on the tensor cores and the raw FP32/FP16 throughput. There is no hardware ray tracing pipeline, so any ray tracing would have to be performed via compute shaders, which is inefficient on this architecture. For a benchmark database, this means that the GRID A100A should be evaluated on compute and tensor performance, not on rasterization or ray tracing metrics. The architecture's strength lies in parallel matrix operations, which the 432 tensor cores accelerate, and the high FP16 rate confirms its intended use case in AI data centers.

How It Compares

As of the data provided, the GRID A100A has no nearest rivals listed in the dataset. The nearestRivals array is empty, and the benchmark scores are also empty, with an average benchmark score of 0. This places the card in a unique position: it is an end-of-life product released in May 2020, and the database does not contain comparative entries for it. The percentile versus all GPUs is 50, which is a neutral midpoint, but without rival data, this percentile lacks context.

The absence of rivals is notable. Typically, a professional accelerator like this would be compared against other data center GPUs, such as the A100 variants or competing AMD Instinct products. However, since no such data is present in the FACT PACK, any comparison would be speculative. The production status is listed as "End-of-life," which suggests that the product has been superseded, but no successor is named. In the absence of direct rivals, the analysis must rely on the absolute specifications. The card's 400 W TDP and 32 GB HBM2e memory put it in a class above consumer gaming GPUs, but without peer benchmarks, its relative standing cannot be quantified. The 50th percentile ranking is the only positional data available, but it is a generic metric that does not indicate performance against specific competitors.

Benchmark Performance

The benchmark data for the GRID A100A is notably sparse: the benchmarks array is empty, and the average benchmark score is 0. This is an unusual situation for a hardware analysis, as there are no synthetic or real-world test results to interpret. The percentile versus all GPUs is 50, which suggests a median position in the overall distribution, but this is a legacy ranking from when the card was active, not a current comparative measure. Given the empty benchmark array, there are no exact percentage deltas to report against rivals, and the nearestRivals list is empty, so no relative performance statements can be made.

What can be inferred comes from the theoretical specifications. The FP32 compute of 19.49 TFLOPS and FP16 of 77.97 TFLOPS are high figures, but without competitor scores, they cannot be placed in context. The texture rate of 609.1 GTexel/s and pixel rate of 270.7 GPixel/s are equally abstract without a reference point. The data suggests that the card was not benchmarked in the current database generation, perhaps due to its end-of-life status or its specialized nature that does not fit standard gaming benchmark suites. In the absence of empirical results, any claim about performance superiority or deficit would be unfounded. The only honest conclusion is that the raw hardware specifications indicate a high-compute device, but its actual benchmark performance remains unquantified within this dataset. Future updates to the database may include legacy benchmark data, but as it stands, the GRID A100A is a hardware entry with defined specs and no measured performance record.