NVIDIA P102-100 vs NVIDIA Tesla P40 Comparison

The NVIDIA Tesla P40 and NVIDIA P102-100 share the same GP102 silicon and Pascal architecture, but they are engineered for entirely different purposes. One is a professional compute card with a massive memory pool; the other is a stripped-down mining part with a crippled interface. The benchmark data shows a clear, if not always dramatic, performance hierarchy between them.

Head-to-Head Benchmarks

In the two available benchmark comparisons, the Tesla P40 wins both, but the margin tells two different stories. The most significant gap appears in the Geekbench OpenCL test, where the Tesla P40 scores 62,017 against the P102-100’s 49,602. That is a 25% advantage for the Tesla P40, a substantial lead that reflects the full configuration of the professional card. In the Geekbench Vulkan test, the results are much closer: the Tesla P40 scores 68,172, while the P102-100 scores 67,454, giving the Tesla P40 a slim 1.1% edge. The Vulkan test appears to be less sensitive to the hardware differences between the two, or perhaps the P102-100’s higher clock speeds help it close the gap in that particular workload.

Looking at the broader context, the Tesla P40’s average benchmark score is 65,095, which places it in the 89th percentile of all GPUs. Its nearest rivals include the AMD Radeon VII (average score 66,004, which is 1.4% higher) and the AMD Radeon Pro WX 9100 (average score 64,212, which is 1.4% lower). The P102-100, by contrast, has an average benchmark score of 58,528, placing it in the 88th percentile. Its closest competitor is the AMD Radeon PRO V710, which scores 58,657—a negligible 0.2% difference. This means that while the Tesla P40 is positioned among high-end workstation and enthusiast cards, the P102-100 sits in a lower performance tier, closer to mid-range gaming and workstation parts.

The 25% OpenCL win for the Tesla P40 is the headline statistic. It suggests that in compute-heavy, general-purpose workloads, the Tesla P40 is significantly faster. The 1.1% Vulkan win, however, indicates that in graphics-oriented APIs, the two cards are nearly interchangeable, likely because the P102-100’s higher boost clock compensates for its reduced core count and memory bandwidth limitations.

Architecture Differences

Both cards are built on the GP102 chip using TSMC’s 16 nm process, with 11,800 million transistors on a 471 mm² die. The transistor density is identical at 25.1M per mm². The fundamental architecture is Pascal, and both support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. Neither card has ray tracing or tensor cores, and both lack display outputs, making them unsuitable for direct display use.

The differences begin with the core configuration. The Tesla P40 has 3,840 shading units, 240 texture mapping units (TMUs), and 96 render output units (ROPs). The P102-100 has fewer: 3,200 shading units, 200 TMUs, and 80 ROPs. This means the Tesla P40 has 20% more shading units, 20% more TMUs, and 20% more ROPs. However, the P102-100 compensates with higher clocks. Its base clock is 1,582 MHz and boost clock is 1,683 MHz, compared to the Tesla P40’s 1,303 MHz base and 1,531 MHz boost. The P102-100’s boost clock is nearly 10% higher than the Tesla P40’s.

Memory is where these cards diverge most sharply. The Tesla P40 comes with 24 GB of GDDR5 on a 384-bit bus, running at 1,808 MHz (7.2 Gbps effective), yielding 347.1 GB/s of bandwidth. The P102-100 has only 5 GB of GDDR5X on a 320-bit bus, running at 1,376 MHz (11 Gbps effective), but its bandwidth is higher at 440.3 GB/s. This is a counterintuitive result: the P102-100 has less memory and a narrower bus, but the faster GDDR5X memory gives it 26.8% more bandwidth. The Tesla P40’s advantage is sheer capacity—24 GB versus 5 GB—which is critical for large datasets.

The resulting theoretical throughput figures reflect these differences. The Tesla P40 has a pixel rate of 147.0 GPixel/s and a texture rate of 367.4 GTexel/s, while the P102-100 manages 134.6 GPixel/s and 336.6 GTexel/s. In floating-point performance, the Tesla P40 delivers 11.76 TFLOPS FP32, and the P102-100 delivers 10.77 TFLOPS FP32. Both have FP16 performance at a 1:64 ratio, with 183.7 GFLOPS for the Tesla P40 and 168.3 GFLOPS for the P102-100.

The power and interface specifications also differ. Both are dual-slot cards with a 250 W TDP and a suggested 600 W PSU. The Tesla P40 uses a single 8-pin EPS connector, while the P102-100 requires two 8-pin connectors. The most striking difference is the bus interface: the Tesla P40 uses PCIe 3.0 x16, while the P102-100 is limited to PCIe 1.0 x4. This is a severe bottleneck for the P102-100, as it limits data transfer to and from the host system, potentially negating some of its raw compute advantages in real-world tasks that require frequent data movement. The P102-100 also lacks a listed height dimension, while the Tesla P40 is specified at 111 mm (4.4 inches) tall; both are 267 mm (10.5 inches) long.

Where Each One Wins

Based on the data, the Tesla P40 is the clear winner in compute-heavy workloads that leverage its full core count and memory capacity. The 25% OpenCL lead is substantial and suggests that tasks like scientific simulation, machine learning inference, or large-scale data processing will favor the Tesla P40. Its 24 GB memory pool is the defining feature here; it allows the card to hold entire models or datasets in VRAM, avoiding costly transfers to system memory. The P102-100’s 5 GB is a hard limit that will force many workloads to spill over, severely impacting performance regardless of its raw compute power.

The P102-100’s higher bandwidth (440.3 GB/s versus 347.1 GB/s) is its one clear advantage. In scenarios where the data fits within its 5 GB frame buffer, the extra bandwidth could provide a speedup in memory-bound operations, such as certain types of image processing or hash-rate-intensive tasks. Its higher clock speeds (1,683 MHz boost versus 1,531 MHz) also give it a slight edge in latency-sensitive, low-occupancy workloads. The near-tie in Vulkan (1.1% delta) suggests that for graphics tasks using that API, the two cards perform essentially the same, with the P102-100’s clocks offsetting its fewer cores.

The P102-100’s PCIe 1.0 x4 interface, however, is a serious liability. Even if the card can compute quickly, getting data to and from it will be slow. This makes the P102-100 unsuitable for general-purpose compute where data is streamed from the CPU or storage. It is a mining-oriented part, designed for workloads that are largely self-contained on the GPU, like cryptographic hashing. In that specific context, the higher bandwidth and clocks might make it competitive, but the data does not include mining benchmarks, so this remains inference from the architecture.

The Tesla P40’s PCIe 3.0 x16 interface is a standard, modern connection that does not throttle data transfer. Combined with its massive memory, this makes it a versatile compute card for professional environments. The Tesla P40’s nearest rival, the AMD Radeon VII, scores 1.4% higher, while the P102-100’s nearest rival, the AMD Radeon PRO V710, scores 0.2% higher. This positions the P40 as a high-end but not top-tier card, while the P102-100 is solidly mid-range.

FAQ

Q: Which card has more raw compute power?

A: The NVIDIA Tesla P40 has higher theoretical FP32 performance at 11.76 TFLOPS, compared to the P102-100’s 10.77 TFLOPS. It also has more shading units (3,840 vs 3,200) and a higher pixel rate (147.0 GPixel/s vs 134.6 GPixel/s).

Q: Does the P102-100 have any performance advantage over the Tesla P40?

A: Yes, in memory bandwidth. The P102-100 delivers 440.3 GB/s versus the Tesla P40’s 347.1 GB/s, thanks to faster GDDR5X memory. It also has higher clock speeds (1,683 MHz boost vs 1,531 MHz), which helps it nearly match the P40 in the Vulkan benchmark (1.1% delta).

Q: Why is the Tesla P40 so much faster in OpenCL?

A: The Tesla P40 scored 62,017 in Geekbench OpenCL, which is 25% higher than the P102-100’s 49,602. This is likely due to its 20% more shading units, 20% more TMUs, and 20% more ROPs, along with its 24 GB memory capacity, which avoids data spills.

Q: Can I use these cards for display output?

A: No. Neither card has display outputs. They are both compute-only or mining-focused cards that require a separate GPU for display.

Q: What is the difference in memory capacity and type?

A: The Tesla P40 has 24 GB of GDDR5 on a 384-bit bus, while the P102-100 has 5 GB of GDDR5X on a 320-bit bus. The P40’s capacity is 19 GB larger, but the P102-100’s bandwidth is 93.2 GB/s higher.

Q: Which card has a better bus interface for general use?

A: The Tesla P40 uses PCIe 3.0 x16, which is standard and fast. The P102-100 uses PCIe 1.0 x4, which is an older, much slower interface that will bottleneck data transfer in most workloads.

The Verdict

The data supports a straightforward conclusion: the NVIDIA Tesla P40 is the superior card for nearly any compute task. It wins both benchmark comparisons, with a decisive 25% lead in OpenCL and a smaller but real 1.1% lead in Vulkan. Its 24 GB memory is a decisive factor for professional workloads, allowing larger datasets to reside on the GPU. The P102-100’s only advantages are higher memory bandwidth and clocks, which do not translate into benchmark wins. Its PCIe 1.0 x4 interface is a severe handicap that will throttle performance in any task requiring substantial host communication.

For anyone choosing between these two, the Tesla P40 is the pick unless the specific workload is extremely memory-bandwidth-sensitive and fits within 5 GB, and even then, the PCIe bottleneck looms large. The P102-100 is a niche mining part with a limited lifespan. The Tesla P40, despite being from 2016, remains a capable compute card with a high 89th percentile ranking. The P102-100, at the 88th percentile, is close in overall standing but falls behind in practical terms due to its interface and memory capacity. If you need a reliable, versatile compute accelerator, the Tesla P40 is the only reasonable choice.

Specification Differences

| Specification | NVIDIA Tesla P40 | NVIDIA P102-100 |

|---|---|---|

| Generation | Tesla Pascal (Pxx) | Mining GPUs |

| Base Clock | 1303 MHz | 1582 MHz |

| Boost Clock | 1531 MHz | 1683 MHz |

| Memory Clock | 1808 MHz (7.2 Gbps effective) | 1376 MHz (11 Gbps effective) |

| Memory Size | 24 GB | 5 GB |

| Memory Type | GDDR5 | GDDR5X |

| Memory Bus Width | 384 bit | 320 bit |

| Memory Bandwidth | 347.1 GB/s | 440.3 GB/s |

| Shading Units | 3840 | 3200 |

| TMUs | 240 | 200 |

| ROPs | 96 | 80 |

| Pixel Rate | 147.0 GPixel/s | 134.6 GPixel/s |

| Texture Rate | 367.4 GTexel/s | 336.6 GTexel/s |

| FP32 Performance | 11.76 TFLOPS | 10.77 TFLOPS |

| FP16 Performance | 183.7 GFLOPS (1:64) | 168.3 GFLOPS (1:64) |

| Power Connectors | 8-pin EPS | 2x 8-pin |

| Bus Interface | PCIe 3.0 x16 | PCIe 1.0 x4 |

| Height | 111 mm (4.4 inches) | Not specified |

| Release Date | 2016-09-12 | 2018-02-11 |

| Launch MSRP | 5,699 USD | Not specified |