NVIDIA P102-100 vs NVIDIA Tesla M40 24 GB Comparison

Head-to-Head Benchmarks

The recorded benchmark data shows a decisive performance gap between these two NVIDIA accelerators. Across both compute and graphics API tests, the NVIDIA P102-100 takes every round, and the margins are substantial.

In Geekbench OpenCL, the P102-100 posts a score of 49,602 against 37,439 for the Tesla M40 24 GB. That works out to a 32.5% advantage for the Pascal-based card. This is not a marginal win; it is a full generation of architectural progress showing up in raw compute throughput. The OpenCL test stresses general-purpose compute workloads, and the P102-100's higher clock speeds and newer architecture clearly carry the day.

The Vulkan result widens the gap even further. The P102-100 scores 67,454, while the Tesla M40 24 GB manages 45,975. The delta here is 46.7% in favor of the P102-100. Vulkan is a lower-level API that tends to expose architectural efficiency differences more directly than older APIs, and the data indicates the Pascal chip handles the workload with far greater effectiveness. A nearly 47% lead in a graphics API test is the kind of margin that changes purchasing decisions.

Looking at the broader database context, the P102-100 sits at the 88th percentile among all GPUs, with an average benchmark score of 58,528. Its nearest rivals include the AMD Radeon PRO V710 at 58,657 (0.2% behind the P102-100), the AMD Radeon RX 6950 XT at 58,392 (0.2% ahead of the P102-100), the Intel Arc A570M at 58,239 (0.5% ahead), and the AMD Radeon RX 5600 OEM at 58,085 (0.8% ahead). In other words, the P102-100 sits in a tight cluster of competitive cards, all within a single percentage point of each other. Its performance class is well established by the data.

The Tesla M40 24 GB, by contrast, lands at the 83rd percentile, with an average score of 41,707. Its nearest rivals are telling: the NVIDIA Tesla M40 (non-24GB variant) scores 41,897, which is 0.5% higher than the 24 GB model. The NVIDIA GeForce RTX 3080 Ti scores 41,187, putting it 1.3% behind the Tesla M40 24 GB. The AMD Radeon Pro 5300 scores 40,870, 2% behind, and the AMD Radeon RX 7650 GRE scores 42,723, 2.4% ahead. The Tesla M40 24 GB is thus clustered with a completely different performance tier, roughly 40% below the P102-100's average score.

The head-to-head numbers tell a simple story. Two benchmarks, two wins for the P102-100. The smallest margin is 32.5%, which is already a decisive generational leap. The largest margin, 46.7% in Vulkan, suggests that the architectural differences show up most strongly in modern, low-overhead graphics workloads.

Where Each One Wins

The P102-100 wins in every measured category. There is no benchmark in the database where the Tesla M40 24 GB comes out ahead. That said, the nature of the wins differs by workload type, and each card has contexts where its characteristics matter more.

For OpenCL compute workloads, the P102-100's 32.5% lead reflects its higher FP32 throughput. The P102-100 delivers 10.77 TFLOPS of FP32 performance, while the Tesla M40 24 GB manages 6.832 TFLOPS. That is a 57.6% raw compute advantage on paper, and the benchmark result of 32.5% reflects real-world efficiency differences. The P102-100 also has a higher texture rate at 336.6 GTexel/s versus 213.5 GTexel/s, which helps in texture-bound compute kernels.

For Vulkan workloads, the P102-100's 46.7% lead is even larger than its OpenCL advantage. This points to architectural efficiency beyond just raw specs. The Pascal architecture's newer instruction scheduling and memory subsystem handling appear to translate into outsized gains in low-level API scenarios. The P102-100's memory bandwidth of 440.3 GB/s, while narrower in bus width at 320 bit, is substantially higher than the Tesla M40 24 GB's 288.4 GB/s from its 384 bit bus. Faster GDDR5X memory at 11 Gbps effective, versus 6 Gbps effective GDDR5, gives the P102-100 a clear bandwidth advantage.

The Tesla M40 24 GB does have one meaningful edge: memory capacity. With 24 GB of VRAM versus 5 GB, it can hold far larger datasets in local memory. For workloads that are capacity-bound rather than bandwidth-bound, such as very large inference batches or datasets that barely fit in memory, the Tesla M40 24 GB can avoid spills to system memory. However, when the data does fit within 5 GB, the P102-100 will process it far faster.

The Tesla M40 24 GB also has a wider memory bus at 384 bit versus 320 bit, but the P102-100's faster memory clock more than compensates. The pixel rate favors the P102-100 at 134.6 GPixel/s versus 106.8 GPixel/s, and the texture rate gap is similar at 336.6 GTexel/s versus 213.5 GTexel/s. The P102-100 also has more shading units (3200 versus 3072) and more texture mapping units (200 versus 192), though the Tesla M40 24 GB has more ROPs (96 versus 80).

Architecture Differences

The P102-100 is built on the GP102 chip using the Pascal architecture, fabricated on a 16 nm process at TSMC. The Tesla M40 24 GB uses the GM200 chip with the Maxwell 2.0 architecture, fabricated on a 28 nm process, also at TSMC. This process shrink from 28 nm to 16 nm is the foundation of the performance gap. The P102-100 packs 11,800 million transistors into a 471 mm² die, achieving a transistor density of 25.1 million transistors per square millimeter. The Tesla M40 24 GB has 8,000 million transistors on a larger 601 mm² die, with a density of only 13.3 million per square millimeter. The Pascal chip is both denser and smaller, a direct result of the newer process node.

Clock speeds show a massive divergence. The P102-100 runs at a base clock of 1582 MHz with a boost of 1683 MHz. The Tesla M40 24 GB runs at 948 MHz base and 1112 MHz boost. That is a 67% higher base clock and a 51% higher boost clock for the P102-100. These clock advantages compound with the architectural improvements to produce the benchmark results seen above.

Memory architecture differs substantially. The P102-100 uses 5 GB of GDDR5X on a 320 bit bus, with memory clocked at 1376 MHz and 11 Gbps effective, producing 440.3 GB/s of bandwidth. The Tesla M40 24 GB uses 24 GB of GDDR5 on a 384 bit bus, with memory at 1502 MHz and 6 Gbps effective, producing 288.4 GB/s. The P102-100 has 52.7% more bandwidth despite having a narrower bus, thanks to the much faster GDDR5X memory. The Tesla M40 24 GB counters with 19 GB more capacity, which is its primary advantage.

Compute resources are close but favor the P102-100. The P102-100 has 3200 shading units, 200 TMUs, and 80 ROPs. The Tesla M40 24 GB has 3072 shading units, 192 TMUs, and 96 ROPs. The P102-100 has 128 more shading units and 8 more TMUs, while the Tesla M40 24 GB has 16 more ROPs. Neither card has ray tracing cores or tensor cores. FP16 performance is listed only for the P102-100 at 168.3 GFLOPS (1:64), meaning it has negligible half-precision throughput; the Tesla M40 24 GB has no listed FP16 capability at all.

The bus interface differs notably. The P102-100 uses PCIe 1.0 x4, which is a severe bottleneck for data transfer to and from the host system. The Tesla M40 24 GB uses PCIe 3.0 x16, a much faster interface. This matters for workloads that stream data, but it does not appear in the on-card benchmarks. Both cards have no display outputs, use dual-slot cooling, and have a 250 W TDP with a 600 W suggested PSU. Both cards are end-of-life. The P102-100 was released in February 2018, while the Tesla M40 24 GB was released in November 2015. The Tesla M40 24 GB lists its predecessor as Tesla Kepler and its successor as Tesla Pascal, which places the P102-100's architecture as the successor generation. The P102-100 is 267 mm long, identical to the Tesla M40 24 GB.

FAQ

Q: Which card is faster in OpenCL compute workloads?

A: The NVIDIA P102-100 scores 49,602 in Geekbench OpenCL, which is 32.5% higher than the Tesla M40 24 GB's 37,439.

Q: How large is the Vulkan performance gap?

A: The P102-100 scores 67,454 in Geekbench Vulkan versus 45,975 for the Tesla M40 24 GB, a 46.7% advantage for the P102-100.

Q: Does the Tesla M40 24 GB have any advantage?

A: Yes, it has 24 GB of memory versus 5 GB on the P102-100. It also has a wider 384 bit memory bus and more ROPs (96 versus 80), plus a faster PCIe 3.0 x16 interface versus PCIe 1.0 x4.

Q: Why is the P102-100 so much faster despite similar shading unit counts?

A: The P102-100 has a 16 nm Pascal chip with a 1582 MHz base and 1683 MHz boost clock, versus the Tesla M40 24 GB's 28 nm Maxwell 2.0 chip at 948 MHz base and 1112 MHz boost. The P102-100 also has 440.3 GB/s bandwidth versus 288.4 GB/s.

Q: Do either of these cards support ray tracing or tensor cores?

A: No. Neither card lists RT cores or tensor cores in the database.

Q: What is the power requirement for each card?

A: Both have a 250 W TDP and a 600 W suggested PSU. The P102-100 uses 2x 8-pin power connectors, while the Tesla M40 24 GB uses an 8-pin EPS connector.

The Verdict

The data is unambiguous. The NVIDIA P102-100 outperforms the NVIDIA Tesla M40 24 GB in every benchmark recorded. The smallest margin is 32.5% in OpenCL, and the largest is 46.7% in Vulkan. The P102-100 also sits at the 88th percentile among all GPUs with an average score of 58,528, while the Tesla M40 24 GB sits at the 83rd percentile with an average score of 41,707.

The P102-100 is the correct choice for any workload that fits within its 5 GB memory capacity and does not require heavy host data transfer, given its PCIe 1.0 x4 interface. Its FP32 throughput of 10.77 TFLOPS, texture rate of 336.6 GTexel/s, and pixel rate of 134.6 GPixel/s all exceed the Tesla M40 24 GB's corresponding figures of 6.832 TFLOPS, 213.5 GTexel/s, and 106.8 GPixel/s. For compute tasks, rendering workloads, or any benchmark in the database, the P102-100 wins.

The Tesla M40 24 GB is only preferable when memory capacity is the binding constraint. Its 24 GB of VRAM is 19 GB more than the P102-100 offers, and its PCIe 3.0 x16 interface is far more practical for host communication. For workloads that require holding very large datasets in GPU memory, or that cannot tolerate the P102-100's PCIe 1.0 x4 bottleneck, the Tesla M40 24 GB remains a functional option. But for raw performance, the P102-100 is the clear pick. The verdict is straightforward: choose the P102-100 for speed, choose the Tesla M40 24 GB only when you need the extra memory capacity.

Specification Differences

| Specification | NVIDIA P102-100 | NVIDIA Tesla M40 24 GB |

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

| Chip | GP102 | GM200 |

| Architecture | Pascal | Maxwell 2.0 |

| Process Node | 16 nm | 28 nm |

| Transistors | 11,800 million | 8,000 million |

| Die Size | 471 mm² | 601 mm² |

| Transistor Density | 25.1M / mm² | 13.3M / mm² |

| Base Clock | 1582 MHz | 948 MHz |

| Boost Clock | 1683 MHz | 1112 MHz |

| Memory Size | 5 GB | 24 GB |

| Memory Type | GDDR5X | GDDR5 |

| Memory Bus | 320 bit | 384 bit |

| Memory Clock | 1376 MHz, 11 Gbps effective | 1502 MHz, 6 Gbps effective |

| Bandwidth | 440.3 GB/s | 288.4 GB/s |

| Shading Units | 3200 | 3072 |

| TMUs | 200 | 192 |

| ROPs | 80 | 96 |

| Pixel Rate | 134.6 GPixel/s | 106.8 GPixel/s |

| Texture Rate | 336.6 GTexel/s | 213.5 GTexel/s |

| FP32 Performance | 10.77 TFLOPS | 6.832 TFLOPS |

| FP16 Performance | 168.3 GFLOPS (1:64) | Not listed |

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

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

| Release Date | February 2018 | November 2015 |

| Predecessor | Not listed | Tesla Kepler |

| Successor | Not listed | Tesla Pascal |

| Geekbench OpenCL | 49,602 | 37,439 |

| Geekbench Vulkan | 67,454 | 45,975 |

| Average Benchmark Score | 58,528 | 41,707 |

| Percentile | 88 | 83 |