NVIDIA A100 SXM4 40 GB vs NVIDIA RTX A4500 Comparison

Head-to-Head Benchmarks

The benchmark data in our database includes two shared tests for these cards: Geekbench OpenCL and Geekbench Vulkan. The NVIDIA A100 SXM4 40 GB wins both, and wins them decisively. In Geekbench OpenCL, the A100 scores 201,096 against the RTX A4500’s 141,837, a 41.8% advantage. That is a substantial gap, one that reflects the A100’s server-class design priorities rather than a close contest. In Geekbench Vulkan, the A100 posts 173,198 versus 129,980, a 33.2% lead. The A100 takes both head-to-head matchups, giving it a clean 2-0 record in direct comparisons.

The RTX A4500 does not win a single recorded test in this pairing, but its raw compute figures tell a slightly different story. The A4500’s FP32 throughput is listed at 23.65 TFLOPS, while the A100 delivers 19.49 TFLOPS. That means the workstation card has a 4.16 TFLOPS advantage in single-precision floating-point work, roughly 21% higher. Yet in the two real-world API benchmarks, the A100 still pulls ahead by significant margins. This suggests the A100’s advantage comes from memory bandwidth and its specialized compute paths, not from raw shader throughput. The A4500 also has more shading units (7,168 versus 6,912), but the A100 counters with a far wider memory bus and HBM2e memory.

The A100’s FP16 output is 77.97 TFLOPS with a 4:1 ratio, while the A4500’s FP16 is 23.65 TFLOPS at a 1:1 ratio. That is a 54.32 TFLOPS difference, or roughly 3.3 times higher for the A100 in half-precision work. This is a clear signal that the A100 is built for AI training and inference workloads where FP16 dominates, whereas the A4500 treats FP16 as a straightforward passthrough with no dedicated tensor acceleration advantage in that mode. The A100’s tensor cores number 432, double the A4500’s 224, which further reinforces this split.

Where Each One Wins

The A100 SXM4 40 GB wins in compute-heavy, memory-bound tasks. Its 1.56 TB/s memory bandwidth is more than double the A4500’s 640.0 GB/s. That bandwidth advantage directly explains the Geekbench OpenCL and Vulkan results, as those tests often stress memory throughput alongside compute. The A100 also has a 5120-bit memory bus versus 320-bit on the A4500, and HBM2e memory versus GDDR6. For large datasets, model training, scientific simulation, or any workload that moves massive amounts of data, the A100’s architecture is clearly superior. Its 40 GB of memory also outpaces the A4500’s 20 GB, meaning larger working sets can reside on-card.

The A4500 wins in conventional graphics and workstation flexibility. It has four DisplayPort 1.4a outputs, while the A100 has no display outputs at all. The A4500 supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, whereas the A100 lists no API support in our database. That makes the A4500 a functional graphics card for visualization, rendering, or any interactive workload. It also has 56 RT cores, giving it hardware-accelerated ray tracing capabilities that the A100 does not expose in our records. The A4500’s higher boost clock of 1650 MHz versus 1410 MHz on the A100 helps in latency-sensitive, lightly threaded tasks.

Power consumption is another clear differentiator. The A4500 is rated at 200 W TDP, while the A100 draws 400 W. The A4500 uses a single 8-pin power connector and fits in a dual-slot form factor, whereas the A100 is an SXM module with no power connectors of its own, requiring a server chassis. The A4500 also lists a suggested PSU of 550 W versus 800 W for the A100. For workstations, the A4500 is the more practical choice; for data centers, the A100 is the expected form factor.

Architecture Differences

The two GPUs share the Ampere architecture but diverge significantly in implementation. The A100 uses the GA100 chip, built on a 7 nm process at TSMC, with 54,200 million transistors on an 826 mm² die. The A4500 uses the GA102 chip, built on an 8 nm process at Samsung, with 28,300 million transistors on a 628 mm² die. The process node difference is small in absolute terms, but the transistor counts reveal the A100’s density advantage: 65.6M transistors per mm² versus 45.1M on the A4500. The A100 packs nearly twice as many transistors into a larger die, and those transistors are dedicated to compute and memory subsystems.

The memory subsystems are fundamentally different. The A100 uses 40 GB of HBM2e over a 5120-bit bus, delivering 1.56 TB/s of bandwidth. The A4500 uses 20 GB of GDDR6 over a 320-bit bus, delivering 640.0 GB/s. The A100’s memory clock is listed at 1215 MHz with 2.4 Gbps effective, while the A4500 runs at 2000 MHz with 16 Gbps effective. Even though the A4500’s memory clock is higher, the A100’s massive bus width gives it the bandwidth crown by a wide margin.

Compute resources also differ. The A100 has 6,912 shading units, 432 TMUs, and 160 ROPs, while the A4500 has 7,168 shading units, 224 TMUs, and 96 ROPs. The A4500 has more shaders but fewer texture and pixel units. The A100’s texture rate is 609.1 GTexel/s versus 369.6 GTexel/s on the A4500, and its pixel rate is 225.6 GPixel/s versus 158.4 GPixel/s. The A100’s tensor core count of 432 doubles the A4500’s 224. The A4500 adds 56 RT cores for ray tracing, which the A100 does not list. The A100 is a compute accelerator with no display path; the A4500 is a full workstation GPU with four DisplayPort outputs.

The A100’s generation is listed as “Server Ampere (Axx)” while the A4500 is “Workstation Ampere (Ax000).” The A100’s predecessor is Tesla Turing and its successor is Server Ada. The A4500’s predecessor is Quadro Turing and its successor is Workstation Ada. Both are end-of-life products, with the A100 releasing on 2020-05-13 and the A4500 on 2021-11-22.

FAQ

Q: Which GPU has higher memory bandwidth?

A: The NVIDIA A100 SXM4 40 GB has 1.56 TB/s of bandwidth over a 5120-bit bus with HBM2e memory. The NVIDIA RTX A4500 has 640.0 GB/s over a 320-bit bus with GDDR6 memory.

Q: Does the RTX A4500 support ray tracing?

A: Yes, the RTX A4500 has 56 RT cores. The A100 SXM4 40 GB does not list any RT cores in the database.

Q: Which card has higher FP32 compute throughput?

A: The RTX A4500 has 23.65 TFLOPS of FP32 throughput, while the A100 SXM4 40 GB has 19.49 TFLOPS. The A4500 is roughly 21% ahead in single-precision work.

Q: Can the A100 SXM4 40 GB drive displays?

A: No, the A100 has no display outputs. The RTX A4500 has four DisplayPort 1.4a outputs.

Q: What is the power draw difference?

A: The A100 SXM4 40 GB is rated at 400 W TDP with a suggested PSU of 800 W. The RTX A4500 is rated at 200 W TDP with a suggested PSU of 550 W.

Q: Which card has more tensor cores?

A: The A100 SXM4 40 GB has 432 tensor cores. The RTX A4500 has 224 tensor cores, exactly half as many.

The Verdict

The data paints a clear picture for distinct audiences. The NVIDIA A100 SXM4 40 GB is the superior compute accelerator. Its 41.8% lead in Geekbench OpenCL and 33.2% lead in Geekbench Vulkan over the RTX A4500 are decisive. Its 1.56 TB/s memory bandwidth, 40 GB of HBM2e, 432 tensor cores, and 77.97 TFLOPS of FP16 throughput make it the obvious choice for AI training, scientific computing, and any workload where memory bandwidth or half-precision math dominates. The A100’s 98th percentile ranking among all GPUs, versus 93rd for the A4500, confirms its position in the upper echelon of our database. Its nearest rivals include the RTX 5000 Ada Generation at 1.3% behind, the A100 SXM4 80 GB at 1.9% behind, and the RTX PRO 5000 Blackwell at 2.8% behind; the A100 leads all of them.

The NVIDIA RTX A4500 is the workstation GPU. It has display outputs, ray tracing cores, DirectX 12 Ultimate support, and a 200 W power envelope that fits in a dual-slot card with a single 8-pin connector. Its 23.65 TFLOPS of FP32 throughput exceeds the A100’s, and its 93rd percentile ranking puts it ahead of 93% of all GPUs in the database. Its nearest rivals are the RTX A4500 Mobile at 0.6% ahead, the AMD Radeon Instinct MI60 at 0.9% ahead, the Quadro GP100 at 4.8% behind, and the Radeon PRO W7600 at 5.2% behind. The A4500 is competitive in its class, but it is not in the A100’s league for raw compute.

For a server rack running AI models or high-performance computing jobs, the A100 SXM4 40 GB is the data-driven pick. For a desktop workstation doing 3D rendering, visualization, or any task that requires a monitor attached to the GPU, the RTX A4500 is the only functional choice of the two. The benchmark results show the A100 winning every shared test, but the A4500 wins on flexibility, power efficiency, and graphics capability. Choose based on the workload, not the scoreboard alone.