NVIDIA A100 PCIe 40 GB

The NVIDIA A100 PCIe 40 GB is a server-class accelerator built on the Ampere architecture, fabricated on a 7 nm process at TSMC with 54,200 million transistors on an 826 mm² die. Positioned as an end-of-life product released on 2020-06-21, it occupies the 50th percentile among all GPUs in the database, with no benchmark scores recorded for direct comparison. Its specifications define a compute-oriented part with no display outputs, a dual-slot design, and a 250 W TDP, powered by a single 8-pin EPS connector with a suggested 600 W power supply.

Benchmark Performance

The A100 PCIe 40 GB’s benchmark profile is defined more by its raw compute ceilings than by any measured workload scores, as the database lists no benchmark results for this accelerator. The FP32 throughput is 19.49 TFLOPS, while FP16 reaches 77.97 TFLOPS via a 4:1 ratio. These figures place the GPU in a distinct class: the FP16 rate is exactly four times the FP32 rate, indicating a deliberate emphasis on mixed-precision workloads rather than traditional rasterization. The pixel rate is 225.6 GPixel/s, and the texture rate is 609.1 GTexel/s, figures that are high but secondary to the compute metrics for a card with no display outputs.

The data shows no nearest rivals, so percentile positioning is the only comparative anchor. The 50th percentile rank against all GPUs suggests a midpoint in the overall distribution, but that rank is misleading for a server part — the percentile likely reflects the absence of gaming-oriented benchmark data rather than a true performance midpoint. The shader configuration comprises 6912 shading units, 432 texture mapping units, and 160 ROPs, with 432 tensor cores. The tensor core count equals the TMU count, underscoring the architecture’s focus on matrix operations. Without rival scores, one cannot state a percentage lead or deficit; the analysis must rest on the internal ratios. The FP16-to-FP32 ratio of 4:1 is typical of Ampere’s server line, where tensor core utilization drives throughput. For context, a 19.49 TFLOPS FP32 ceiling is substantial for a 250 W envelope, but the absence of benchmark data means no real-world workload comparisons are possible from this pack.

Who Should Consider It

Given the lack of display outputs and the compute-oriented specifications, this accelerator is not intended for interactive graphics. The 40 GB HBM2e memory and 1.56 TB/s bandwidth target large data residency, making it suitable for workloads that exceed the capacity of typical consumer cards. At high resolutions, the memory subsystem becomes the binding constraint for many compute tasks — the 5120-bit bus width and 1.56 TB/s bandwidth allow the GPU to feed its 6912 shading units without stalling, which is critical for dense matrix operations or large batch processing. The FP16 throughput of 77.97 TFLOPS suggests a primary use case in AI training or inference where 4:1 precision scaling is exploited. For users running FP32 workloads, the 19.49 TFLOPS rate is the relevant figure, and the 250 W TDP means a single 8-pin EPS connector suffices — the suggested 600 W power supply provides headroom for the rest of the system.

The data does not include resolution-specific frame rate projections, so recommendations must be inferred from the hardware profile. The 40 GB VRAM and 1.56 TB/s bandwidth are excessive for 1080p or 1440p gaming, and the lack of display outputs precludes direct use. Instead, the target audience is server administrators or researchers deploying batch inference or training jobs where memory capacity exceeds 24 GB. The 7 nm process and 54,200 million transistors on an 826 mm² die indicate a high transistor density of 65.6M per mm², which correlates with high compute efficiency per watt, but the 250 W TDP is modest for the throughput on offer. The dual-slot form factor and 267 mm length (10.5 inches) fit standard server chassis, with a height of 111 mm (4.4 inches). The PCIe 4.0 x16 interface is current but not bleeding-edge; systems with older PCIe 3.0 slots would see reduced transfer rates, though the compute performance itself is not bottlenecked by the interface for most in-memory workloads.

Ray Tracing and Feature Set

The A100 PCIe 40 GB has no dedicated ray tracing cores — the fact pack lists `rtCores: null`. This absence is consistent with its server positioning; ray tracing acceleration is not a priority for compute accelerators. The tensor cores, numbering 432, are the primary feature for AI workloads, enabling the 77.97 TFLOPS FP16 rate. The API support is notably sparse: DirectX, OpenGL, and Vulkan are all listed as null. This means the card exposes no standard graphics APIs, reinforcing that it is not a rendering device. The lack of display outputs further confirms this — there is no way to connect a monitor, and the card is designed for headless operation in data centers. The architecture is Ampere, with the GA100 chip, and the generation is “Server Ampere (Axx),” which places it in a lineage distinct from consumer Ampere parts. The predecessor is listed as Tesla Turing, and the successor is Server Ada, indicating a clear generational handoff.

The tensor cores are not described with a specific generation or throughput per core, but the aggregate FP16 figure of 77.97 TFLOPS is the headline number for AI tasks. The 4:1 ratio means that FP32 workloads run at 19.49 TFLOPS, which is still high but not the primary target. The absence of RT cores means any ray tracing workload would rely on compute shaders, which is inefficient but possible in theory — the hardware has no fixed-function acceleration. For users evaluating this card for deep learning, the tensor cores and 40 GB memory are the key features; for anyone expecting gaming or content creation capabilities, the null API list and no display outputs are disqualifying.

FAQ

Q: What is the memory capacity and type of this GPU?

A: The GPU has 40 GB of HBM2e memory with a 5120-bit bus width, providing 1.56 TB/s of bandwidth.

Q: Does this card support DirectX or Vulkan?

A: No. The fact pack lists DirectX, OpenGL, and Vulkan as null, meaning no standard graphics APIs are exposed.

Q: How many tensor cores does it have, and what is the FP16 throughput?

A: It has 432 tensor cores, and the FP16 throughput is 77.97 TFLOPS (4:1 ratio), while FP32 is 19.49 TFLOPS.

Q: Is ray tracing supported?

A: No dedicated ray tracing cores are present — the `rtCores` field is null. The card is not designed for ray traced workloads.

Q: What is the power requirement?

A: The TDP is 250 W, with a single 8-pin EPS power connector and a suggested 600 W power supply.

Q: What is the physical size and slot width?

A: It is a dual-slot card with a length of 267 mm (10.5 inches) and a height of 111 mm (4.4 inches).

Memory Subsystem

The memory subsystem is a defining characteristic of the A100 PCIe 40 GB. It uses 40 GB of HBM2e across a 5120-bit bus, yielding 1.56 TB/s of bandwidth. The memory clock is 1215 MHz, with an effective data rate of 2.4 Gbps. This configuration is unusual in its capacity-bus balance: the 5120-bit width is wider than most consumer parts, and the 1.56 TB/s bandwidth is directly tied to that width and clock. For high-resolution workloads, the practical implication is that the GPU can move large datasets on and off chip faster than the compute units can consume them in many cases. The 40 GB capacity allows entire model weights or large batch buffers to reside in VRAM, avoiding PCIe transfers that would otherwise bottleneck at the PCIe 4.0 x16 interface.

The pixel rate of 225.6 GPixel/s and texture rate of 609.1 GTexel/s are derived from the ROP and TMU counts (160 and 432, respectively) and the boost clock of 1410 MHz. These rates are secondary to the memory bandwidth for compute tasks, but they indicate that the card could handle framebuffer operations if needed — though the lack of display outputs renders that moot. The memory type HBM2e is stacked, which explains the 826 mm² die size and 54,200 million transistors; the memory itself is not on the same die but integrated in the package. For users running FP16 workloads, the 77.97 TFLOPS rate can be sustained only if the memory bandwidth feeds the tensor cores adequately — the 1.56 TB/s figure is sufficient for that purpose, as the FP16 compute-to-bandwidth ratio is roughly 50 bytes per FLOP (1.56 TB/s divided by 77.97 TFLOPS). In comparison, the FP32 ratio is about 80 bytes per FLOP (1.56 TB/s divided by 19.49 TFLOPS), meaning FP32 workloads are more memory-bound relative to their compute ceiling. This asymmetry is typical for server accelerators: the memory subsystem is sized for the higher-precision throughput. The 40 GB capacity also exceeds the 24 GB found on many competing accelerators, but since no rivals are listed, any comparative claim would be speculative. The bus width of 5120 bit is the widest in the fact pack, and the effective memory clock of 2.4 Gbps is low by consumer standards, but the breadth of the bus compensates, delivering the 1.56 TB/s aggregate.