NVIDIA A2 vs NVIDIA Tesla P4 Comparison

The NVIDIA Tesla P4 and NVIDIA A2 are both end-of-life, single-slot, accelerator cards with no display outputs, but they serve fundamentally different workloads. The benchmark data shows a clean split: the Tesla P4 wins the Vulkan compute test by a wide margin, while the A2 edges ahead in OpenCL. The A2 is the more modern, feature-rich card with a massive memory capacity advantage, but the Tesla P4 delivers superior raw graphics-API compute performance. The choice between them depends entirely on whether the workload prioritizes raw throughput in a specific API or benefits from newer architecture features and double the memory.

Where Each One Wins

The Tesla P4 is the clear winner for graphics-oriented compute tasks that leverage the Vulkan API. In the geekbench_vulkan test, the Tesla P4 scores 40309, which is 18.5% higher than the A2's 34023. This is a dominant margin that indicates the Pascal architecture's design is particularly well-suited to Vulkan's execution model. The P4's higher pixel rate (71.30 GPixel/s vs 56.64 GPixel/s) and texture rate (178.2 GTexel/s vs 70.80 GTexel/s) support this, as these are the throughput metrics that matter most for graphics-adjacent compute.

The A2 wins the geekbench_opencl test, but by a much narrower margin. It scores 35357 versus the P4's 34947, a delta of just 1.2%. This is effectively a statistical tie, and the A2's victory here is not a performance statement but rather a reflection of its architectural efficiency. The A2's win is more about its modern Ampere design and its ability to handle OpenCL workloads with better power efficiency, as evidenced by its lower 60 W TDP compared to the P4's 75 W.

The real differentiator is memory capacity. The A2 ships with 16 GB of GDDR6, double the P4's 8 GB of GDDR5. For workloads that require large model residency or massive datasets, the A2 is the only viable option. The P4's 8 GB is sufficient for many inference tasks, but it will hit a wall where the A2 continues. The A2 also has a slightly higher memory bandwidth (200.1 GB/s vs 192.3 GB/s), which helps in memory-bound scenarios.

Architecture Differences

The architectural gap between these two cards is generational. The Tesla P4 is built on the Pascal architecture using the GP104 chip, fabricated on TSMC's 16 nm process. The A2 uses the Ampere architecture with the GA107 chip, fabricated on Samsung's 8 nm process. This process shrink allows the A2 to pack 8,700 million transistors into a 200 mm² die, resulting in a transistor density of 43.5M / mm². The P4 has 7,200 million transistors on a much larger 314 mm² die, giving it a density of just 22.9M / mm². The A2 is more than twice as dense.

The A2's Ampere architecture brings hardware features that the P4 completely lacks. The A2 includes 10 RT cores and 40 tensor cores, enabling hardware-accelerated ray tracing and tensor operations. The P4 has neither. This is a decisive factor for any workload involving neural network inference or AI acceleration, as the tensor cores provide dedicated hardware that the P4 must emulate with its general-purpose shaders.

The compute capabilities tell a nuanced story. The P4 has 2560 shading units, 160 TMUs, and 64 ROPs, while the A2 has only 1280 shading units, 40 TMUs, and 32 ROPs. Despite having half the shaders, the A2's FP32 performance is 4.531 TFLOPS, which is not far behind the P4's 5.704 TFLOPS. This is due to the A2's much higher clocks: 1440 MHz base and 1770 MHz boost, versus the P4's 886 MHz base and 1114 MHz boost. However, the FP16 performance is where the A2 truly separates itself. The A2 achieves 4.531 TFLOPS FP16, a 1:1 ratio with its FP32, while the P4 manages only 89.12 GFLOPS FP16, a 1:64 ratio. This makes the A2 vastly superior for FP16 workloads.

Head-to-Head Benchmarks

The geekbench_vulkan test is the single largest performance gap between these two cards. The Tesla P4 scores 40309, which is an 18.5% advantage over the A2's 34023. This is not a marginal difference; it is a decisive victory that places the P4 in a different performance class for Vulkan compute. The P4's architecture, with its 2560 shaders and high texture fill rate, appears to be better optimized for Vulkan's command buffer and descriptor set handling.

The geekbench_opencl test tells a different story. The A2 wins with a score of 35357, but the margin over the P4's 34947 is only 1.2%. This is within the margin of error and suggests that both cards are effectively equivalent in OpenCL performance. The A2's win here is likely due to its higher clock speeds and more efficient memory subsystem, but the practical difference is negligible for real-world workloads.

Looking at the broader benchmark context, the P4's average benchmark score is 37628, placing it in the 81st percentile of all GPUs. Its nearest rival is the NVIDIA GeForce RTX 4070, which scores 37648, a delta of -0.1%. The A2's average score is 34690, placing it in the 79th percentile. Its nearest rival is the NVIDIA T1000 8 GB, which scores 34561, a delta of 0.4%. The P4's higher average score and higher percentile rank indicate that it is the more powerful card overall, despite the A2's architectural advantages.

FAQ

Q: Which card has better overall benchmark performance?

A: The NVIDIA Tesla P4 has a higher average benchmark score of 37628 compared to the NVIDIA A2's 34690. The P4 also ranks in the 81st percentile of all GPUs, while the A2 ranks in the 79th.

Q: Is the NVIDIA A2 faster in any benchmark?

A: Yes, the A2 wins the geekbench_opencl test with a score of 35357, which is 1.2% higher than the P4's 34947. However, this margin is very small.

Q: How much faster is the Tesla P4 in Vulkan?

A: The Tesla P4 scores 40309 in geekbench_vulkan, which is 18.5% higher than the A2's 34023. This is the largest performance gap between the two cards.

Q: Does the A2 support ray tracing or tensor operations?

A: Yes, the NVIDIA A2 includes 10 RT cores and 40 tensor cores, which enable hardware-accelerated ray tracing and tensor operations. The Tesla P4 has neither.

Q: What is the memory capacity difference?

A: The NVIDIA A2 has 16 GB of GDDR6 memory, while the Tesla P4 has 8 GB of GDDR5 memory. The A2 also has a slightly higher memory bandwidth at 200.1 GB/s versus 192.3 GB/s.

Q: Which card is more power efficient?

A: The NVIDIA A2 has a lower TDP of 60 W compared to the Tesla P4's 75 W. Both cards are single-slot and require no power connectors.

Specification Differences

The two cards differ across nearly every core specification. The Tesla P4 uses the GP104 chip on TSMC's 16 nm process, while the A2 uses the GA107 chip on Samsung's 8 nm process. The P4 has a larger die at 314 mm² versus the A2's 200 mm², but the A2 has a higher transistor count at 8,700 million versus 7,200 million.

The memory subsystems are fundamentally different. The P4 has 8 GB of GDDR5 on a 256-bit bus, while the A2 has 16 GB of GDDR6 on a 128-bit bus. The A2's bandwidth of 200.1 GB/s slightly exceeds the P4's 192.3 GB/s. The P4's memory runs at 1502 MHz (6 Gbps effective), while the A2's runs at 1563 MHz (12.5 Gbps effective).

The compute unit counts are drastically different. The P4 has 2560 shading units, 160 TMUs, and 64 ROPs. The A2 has 1280 shading units, 40 TMUs, and 32 ROPs. The P4 also has higher pixel and texture rates: 71.30 GPixel/s and 178.2 GTexel/s, versus the A2's 56.64 GPixel/s and 70.80 GTexel/s.

Clock speeds favor the A2 significantly. The A2 has a base clock of 1440 MHz and a boost clock of 1770 MHz, while the P4 has a base clock of 886 MHz and a boost clock of 1114 MHz. However, the P4 still achieves higher FP32 performance at 5.704 TFLOPS versus 4.531 TFLOPS. The FP16 comparison is stark: the A2 achieves 4.531 TFLOPS, while the P4 manages only 89.12 GFLOPS.

The A2 supports a newer API set, including DirectX 12 Ultimate (12_2), while the P4 supports DirectX 12 (12_1). Both support OpenGL 4.6 and Vulkan 1.4. The bus interfaces also differ: the P4 uses PCIe 3.0 x16, while the A2 uses PCIe 4.0 x8. The TDP favors the A2 at 60 W versus 75 W for the P4.

The Verdict

The data indicates a clear split based on workload type. For Vulkan-based compute tasks, the Tesla P4 is the superior choice. Its 18.5% advantage in the Vulkan benchmark is substantial and cannot be overcome by the A2's architectural features. The P4 also has higher average benchmark scores overall (37628 vs 34690) and a higher percentile rank (81st vs 79th), making it the more powerful card in raw compute terms.

The A2 wins for modern, feature-dependent workloads. Its 16 GB of memory is double the P4's 8 GB, and its tensor cores and RT cores enable hardware acceleration that the P4 cannot provide. The A2's FP16 performance is 4.531 TFLOPS, which is 50 times higher than the P4's 89.12 GFLOPS. For AI inference, machine learning, or any workload that leverages tensor operations, the A2 is the only option.

The OpenCL benchmark is essentially a tie, with the A2 winning by just 1.2%. This means the choice cannot be made on OpenCL performance alone. The decision should be based on whether the workload is graphics-API compute (Vulkan) or modern AI/RT-accelerated compute. The Tesla P4 is the pick for Vulkan-heavy pipelines, while the A2 is the pick for memory-hungry, FP16, or tensor-heavy workloads. The data does not support a single universal winner; it supports a workload-specific verdict.