NVIDIA A10G vs NVIDIA Tesla P100 PCIe 16 GB Comparison

The NVIDIA A10G and the NVIDIA Tesla P100 PCIe 16 GB represent two distinct eras of NVIDIA’s accelerator lineup. The A10G, built on the Ampere architecture, is a newer, more efficient part, while the P100 is a Pascal-generation workhorse from an older generation. Benchmark data in the database shows a clear overall performance leader, but the two cards have different architectural priorities that matter for specific workloads.

The recorded data shows the A10G achieving an average benchmark score of 151,963, placing it in the 97th percentile of all GPUs tracked. The P100, in contrast, scores 79,605, landing in the 92nd percentile. In the single head-to-head benchmark available, the Geekbench OpenCL test, the A10G scores 158,063 against the P100’s 79,605, a delta of 98.6%. This is a near-doubling of raw compute throughput in that specific test. However, the P100 retains a unique advantage in memory bandwidth, a critical factor for certain high-performance computing tasks.

Where Each One Wins

The A10G wins outright in raw compute throughput. Its FP32 performance is listed at 31.52 TFLOPS, more than three times the P100’s 9.526 TFLOPS. For any workload relying on single-precision floating-point math, such as standard AI inference, graphics rendering, or general scientific simulation, the A10G is the decisive choice. Its FP16 performance is equally strong: the A10G delivers 31.52 TFLOPS with a 1:1 ratio, meaning it does not compromise when switching to reduced precision. The P100’s FP16 output is 19.05 TFLOPS, but that figure comes with a 2:1 ratio, indicating it is achieved by splitting the FP32 units, which can reduce throughput in mixed workloads.

The P100 wins in memory bandwidth. The database lists the P100 with 732.2 GB/s of bandwidth, driven by a 4096-bit bus and HBM2 memory. The A10G, despite having a larger 24 GB pool of GDDR6, offers 600.2 GB/s. That 132 GB/s difference is substantial. Applications that are memory-bound, such as large sparse matrix operations, certain database workloads, or high-resolution convolution layers that do not fit in cache, will see a smaller gap in real-world performance than the raw TFLOPS suggest. The P100 also has a higher pixel rate per clock relative to its architecture, but the A10G still produces more absolute pixel throughput (164.2 GPixel/s versus 127.6 GPixel/s).

The A10G also wins in feature support. It includes 72 RT cores and 288 tensor cores, while the P100 has none of either. Any workload leveraging ray tracing or tensor-core-accelerated operations, such as DLSS or certain linear algebra libraries, is exclusive to the A10G. The P100 can still run such code via fallback paths, but the hardware acceleration is absent.

Architecture Differences

The A10G is built on the GA102 chip using the Ampere architecture, fabricated on an 8 nm process at Samsung. It packs 28,300 million transistors into a 628 mm² die, yielding a transistor density of 45.1 million per square millimeter. The P100 uses the GP100 chip, a Pascal design, on a 16 nm process from TSMC. It has 15,300 million transistors on a 610 mm² die, for a density of 25.1 million per square millimeter. The A10G’s newer process allows it to nearly double the transistor count on a similarly sized die.

The core configurations differ drastically. The A10G has 9,216 shading units, 288 texture mapping units, and 96 render output units. The P100 has 3,584 shading units, 224 TMUs, and 96 ROPs. The A10G has 288 tensor cores and 72 RT cores, while the P100 has no dedicated tensor or RT hardware. This explains the massive FP32 advantage, as the A10G has 2.57 times the shading units.

Memory architecture is another key split. The A10G uses 24 GB of GDDR6 on a 384-bit bus, with a memory clock of 1563 MHz (12.5 Gbps effective). The P100 uses 16 GB of HBM2 on a 4096-bit bus, with a memory clock of 715 MHz (1430 Mbps effective). The P100’s wider bus compensates for its slower clock, resulting in higher total bandwidth. The A10G’s higher clock speed and newer memory type provide lower latency per access, but the raw throughput favors the P100.

Power and physical design also reflect their generations. The A10G has a TDP of 150 W, is single-slot, and uses an 8-pin EPS connector. The P100 has a TDP of 250 W, is dual-slot, and uses a single 8-pin connector. The A10G’s lower power draw is notable given its higher performance, a direct benefit of the 8 nm process. Both cards are end-of-life in production status, but the A10G was released in 2021, while the P100 came out in 2016.

Head-to-Head Benchmarks

The database contains one direct head-to-head benchmark: Geekbench OpenCL. The A10G scores 158,063, while the P100 scores 79,605. The delta is 98.6%, meaning the A10G achieves nearly double the score. This is the clearest single metric of overall compute capability. The A10G’s 97th percentile ranking versus the P100’s 92nd percentile reinforces this gap, though the percentile difference is smaller than the raw score difference, indicating that the P100 still holds its own against the broader GPU population of its era.

Breaking down the score, the A10G’s advantage comes from its 31.52 TFLOPS FP32 throughput. The P100’s 9.526 TFLOPS is less than a third of that. In a typical OpenCL workload that exercises both integer and floating-point operations, the A10G’s superior shading unit count and tensor core availability drive the result. The P100’s higher memory bandwidth does not close the gap in this synthetic test, as the workload is compute-bound rather than memory-bound.

There is no head-to-head Vulkan benchmark in the database for the P100, so the A10G’s standalone Vulkan score of 145,863 cannot be directly compared. However, given the OpenCL result, the A10G’s Vulkan performance would be expected to follow a similar pattern, as both APIs expose the same hardware execution units.

The A10G’s nearest rivals in the database are instructive. The Tesla V100 PCIe 32 GB scores 150,305, only 1.1% behind the A10G. The A100 PCIe 40 GB scores 162,504, 6.5% ahead. The AMD Radeon Pro W6800X scores 160,671, 5.4% ahead. The AMD Instinct MI100 scores 139,035, 9.3% behind. The A10G sits in a competitive middle ground, close to the V100 but below the A100 and W6800X.

The P100’s rivals show a different landscape. The Tesla P100 PCIe 12 GB scores 79,396, just 0.3% behind. The GeForce RTX 5090 scores 79,842, 0.3% ahead. The AMD Radeon RX 6850M XT scores 78,940, 0.8% ahead of the P100. The AMD Radeon Pro Vega 64X scores 80,959, 1.7% ahead. The P100 is effectively at parity with these cards, none of which are modern data center parts. This suggests the P100’s performance is comparable to a contemporary consumer or mobile GPU, not a current accelerator.

Specification Differences

The two cards differ in nearly every major specification category. Below are the fields where the database records distinct values.

Architecture and Process: The A10G uses Ampere (GA102) on an 8 nm Samsung process. The P100 uses Pascal (GP100) on a 16 nm TSMC process.

Transistors and Die: The A10G has 28,300 million transistors on a 628 mm² die. The P100 has 15,300 million transistors on a 610 mm² die. Transistor density is 45.1M/mm² for the A10G versus 25.1M/mm² for the P100.

Clocks: The A10G has a base clock of 1320 MHz and boost of 1710 MHz. The P100 has a base clock of 1190 MHz and boost of 1329 MHz. Memory clock is 1563 MHz (12.5 Gbps effective) for the A10G versus 715 MHz (1430 Mbps effective) for the P100.

Memory: The A10G has 24 GB of GDDR6 on a 384-bit bus, yielding 600.2 GB/s bandwidth. The P100 has 16 GB of HBM2 on a 4096-bit bus, yielding 732.2 GB/s bandwidth.

Compute Units: The A10G has 9,216 shading units, 288 TMUs, 96 ROPs, 72 RT cores, and 288 tensor cores. The P100 has 3,584 shading units, 224 TMUs, 96 ROPs, and no RT or tensor cores.

Performance Rates: The A10G has a pixel rate of 164.2 GPixel/s and texture rate of 492.5 GTexel/s. The P100 has a pixel rate of 127.6 GPixel/s and texture rate of 297.7 GTexel/s. FP32 is 31.52 TFLOPS for the A10G versus 9.526 TFLOPS for the P100. FP16 is 31.52 TFLOPS (1:1) for the A10G versus 19.05 TFLOPS (2:1) for the P100.

Power and Physical: The A10G has a TDP of 150 W, is single-slot, and uses an 8-pin EPS connector. The P100 has a TDP of 250 W, is dual-slot, and uses a single 8-pin connector. Suggested PSU is 450 W for the A10G and 600 W for the P100.

Interface and APIs: The A10G uses PCIe 4.0 x16, supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The P100 uses PCIe 3.0 x16, supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3.

Dimensions: Both are 267 mm long (10.5 inches). The A10G is 112 mm high (4.4 inches). The P100’s height is not recorded.

Release and Status: The A10G was released on 2021-04-11, with a predecessor of Tesla Turing and successor of Server Ada. The P100 was released on 2016-06-19, with a predecessor of Tesla Maxwell and successor of Tesla Volta. The A10G has no recorded launch MSRP, while the P100’s launch MSRP is 5,699 USD.

FAQ

Q: Which card is faster in raw compute?

A: The NVIDIA A10G. In the Geekbench OpenCL benchmark, it scores 158,063 versus the P100’s 79,605, a 98.6% advantage. Its FP32 throughput is 31.52 TFLOPS compared to 9.526 TFLOPS for the P100.

Q: Does the P100 have any advantage over the A10G?

A: Yes, in memory bandwidth. The P100 delivers 732.2 GB/s over a 4096-bit HBM2 bus, while the A10G provides 600.2 GB/s over a 384-bit GDDR6 bus. For memory-bound workloads, this can narrow the performance gap.

Q: Does the P100 support tensor cores or ray tracing?

A: No. The P100 has no RT cores and no tensor cores. The A10G includes 72 RT cores and 288 tensor cores, which enable hardware-accelerated ray tracing and tensor operations.

Q: How do their power requirements compare?

A: The A10G has a TDP of 150 W and suggests a 450 W PSU. The P100 has a TDP of 250 W and suggests a 600 W PSU. The A10G is also single-slot, while the P100 is dual-slot.

Q: What is the memory capacity difference?

A: The A10G has 24 GB of GDDR6, while the P100 has 16 GB of HBM2. The A10G has 50% more capacity, but the P100 has higher bandwidth per byte due to its wider bus.

Q: How does each card rank against all GPUs in the database?

A: The A10G is in the 97th percentile, while the P100 is in the 92nd percentile. The A10G’s average benchmark score is 151,963; the P100’s is 79,605.