NVIDIA P102-100 vs NVIDIA Tesla T4 Comparison

The NVIDIA Tesla T4 and NVIDIA P102-100 are both end-of-life server accelerators from NVIDIA, but they target fundamentally different workloads. The T4 is a Turing-based compute card designed for inference and virtualized environments, while the P102-100 is a Pascal-based mining card stripped of display outputs. Benchmark data reveals a clear performance hierarchy, with the T4 leading in both available tests, though the margin varies significantly by API.

Head-to-Head Benchmarks

The Tesla T4 wins both head-to-head benchmark comparisons, but the size of the victory depends heavily on the workload. In Geekbench OpenCL, the T4 scores 61,276 against the P102-100’s 49,602, a decisive 23.5% advantage. This is a substantial gap that reflects the T4’s architectural efficiency in compute-heavy tasks. The Vulkan test tells a different story: the T4 scores 72,190 versus 67,454 for the P102-100, a narrower 7% lead. While still a win, this smaller margin suggests that the P102-100’s raw shader throughput can partially compensate for its older architecture in certain graphics-oriented workloads.

Looking at overall averages, the T4’s average benchmark score is 66,733, placing it in the 90th percentile of all GPUs. The P102-100’s average is 58,528, which puts it in the 88th percentile. The T4’s nearest rivals include the AMD Radeon VII (66,004, just 1.1% behind) and the AMD Radeon Instinct MI25 (68,562, 2.7% ahead). The P102-100, by contrast, sits in a tight cluster with the AMD Radeon PRO V710 (58,657, 0.2% behind) and the AMD Radeon RX 6950 XT (58,392, 0.2% ahead). These proximity scores show that the T4 competes in a higher performance tier overall, while the P102-100 is bracketed by mid-range cards.

The Vulkan results are particularly interesting for the P102-100. Its score of 67,454 is only 7% below the T4’s 72,190, and this is the card’s strongest showing. The OpenCL result, however, exposes a weakness: a 23.5% deficit indicates that the P102-100’s compute performance does not scale as well in OpenCL workloads. For users prioritizing OpenCL compute, the T4 is the clear choice; for Vulkan-based tasks, the P102-100 remains competitive despite its age.

Architecture Differences

The two cards are built on different architectures, process nodes, and chip designs. The Tesla T4 uses the TU104 chip based on the Turing architecture, fabricated on a 12 nm process at TSMC. It packs 13,600 million transistors on a 545 mm² die, yielding a transistor density of 25.0M per mm². The P102-100 uses the GP102 chip based on the older Pascal architecture, also from TSMC but on a 16 nm node. It contains 11,800 million transistors across a 471 mm² die, with a slightly higher density of 25.1M per mm².

Clock speeds differ dramatically. The T4 has a base clock of 585 MHz and a boost clock of 1590 MHz, while the P102-100 runs at a much higher 1582 MHz base and 1683 MHz boost. Memory clocks also favor the P102-100: its GDDR5X memory operates at 1376 MHz (11 Gbps effective), whereas the T4’s GDDR6 runs at 1250 MHz (10 Gbps effective). However, the T4 compensates with a larger 16 GB memory pool versus 5 GB, and a wider 256-bit bus versus 320-bit, resulting in lower bandwidth: 320.0 GB/s for the T4 versus 440.3 GB/s for the P102-100.

Compute resources favor the P102-100 in raw counts. The Pascal card has 3200 shading units, 200 TMUs, and 80 ROPs, compared to the T4’s 2560 shading units, 160 TMUs, and 64 ROPs. The P102-100 also has higher pixel rate (134.6 GPixel/s vs 101.8 GPixel/s) and texture rate (336.6 GTexel/s vs 254.4 GTexel/s). In raw FP32 throughput, the P102-100 delivers 10.77 TFLOPS versus 8.141 TFLOPS for the T4. Yet the T4 has dedicated hardware the P102-100 lacks entirely: 40 RT cores and 320 tensor cores. The T4 also offers FP16 performance of 16.28 TFLOPS (2:1 ratio), while the P102-100’s FP16 is a meager 168.3 GFLOPS (1:64 ratio), a 97-fold difference.

Power and physical design diverge sharply. The T4 is a single-slot card with a 70 W TDP and no power connectors, requiring only a 250 W suggested PSU. The P102-100 is dual-slot, rated at 250 W, needs two 8-pin connectors, and demands a 600 W PSU. The T4 is also shorter at 168 mm (6.6 inches) versus 267 mm (10.5 inches) for the P102-100. Both cards lack display outputs, but their bus interfaces differ: the T4 uses PCIe 3.0 x16, while the P102-100 is limited to PCIe 1.0 x4, a significant bottleneck for data transfer.

Where Each One Wins

The Tesla T4 wins in virtually every computational scenario that matters for modern workloads. Its 23.5% OpenCL lead is the headline result, showing clear superiority in compute-heavy tasks like inference, scientific simulation, and general-purpose GPU computing. The presence of tensor cores and RT cores makes the T4 uniquely suited for AI inference and ray-traced rendering, neither of which the P102-100 can accelerate. The T4’s 16 GB memory capacity is double-plus the P102-100’s 5 GB, enabling larger datasets and models to reside on-card. Its FP16 throughput of 16.28 TFLOPS is transformative for mixed-precision workloads, while the P102-100’s 168.3 GFLOPS is effectively non-functional for such tasks.

The P102-100’s strengths are narrower but real. Its 440.3 GB/s memory bandwidth exceeds the T4’s 320.0 GB/s, which benefits memory-bound operations. Its higher clock speeds (1683 MHz boost) and greater shader count (3200) give it an edge in raw fill-rate tasks, evidenced by its 134.6 GPixel/s pixel rate. In Vulkan, the P102-100 comes within 7% of the T4, so for Vulkan-based rendering or compute, it remains a viable option. Its 10.77 TFLOPS FP32 is 32% higher than the T4’s 8.141 TFLOPS, making it faster for pure FP32 workloads that do not leverage tensor or RT cores.

For deployment, the T4 is the obvious choice in power-constrained environments: it draws 70 W versus 250 W, needs no auxiliary power, and fits in a single slot. The P102-100’s 250 W TDP and dual-slot footprint, combined with its PCIe 1.0 x4 interface, make it a poor fit for modern servers. The T4’s PCIe 3.0 x16 connection is standard and far more practical for data transfer. In summary, the T4 wins for AI, FP16, and energy-efficient compute; the P102-100 wins for raw FP32 throughput and memory bandwidth, but only in scenarios that can tolerate its power and interface limitations.

FAQ

Q: Which card has higher raw FP32 performance?

A: The NVIDIA P102-100 delivers 10.77 TFLOPS FP32, which is 32% higher than the Tesla T4’s 8.141 TFLOPS.

Q: How much faster is the Tesla T4 in OpenCL benchmarks?

A: The T4 scores 61,276 in Geekbench OpenCL versus 49,602 for the P102-100, a 23.5% advantage.

Q: Can the P102-100 do ray tracing or tensor operations?

A: No. The P102-100 has no RT cores and no tensor cores, while the T4 includes 40 RT cores and 320 tensor cores.

Q: What is the memory capacity difference?

A: The T4 has 16 GB of GDDR6 memory, while the P102-100 has 5 GB of GDDR5X, a 11 GB difference in favor of the T4.

Q: Which card requires more power?

A: The P102-100 has a 250 W TDP and needs two 8-pin connectors, while the T4 has a 70 W TDP and requires no power connectors.

Q: How close are their Vulkan scores?

A: The T4 scores 72,190 and the P102-100 scores 67,454 in Geekbench Vulkan, a 7% difference in favor of the T4.

Specification Differences

| Specification | NVIDIA Tesla T4 | NVIDIA P102-100 |

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

| Architecture | Turing | Pascal |

| Process Node | 12 nm | 16 nm |

| Chip | TU104 | GP102 |

| Transistors | 13,600 million | 11,800 million |

| Die Size | 545 mm² | 471 mm² |

| Base Clock | 585 MHz | 1582 MHz |

| Boost Clock | 1590 MHz | 1683 MHz |

| Memory Clock | 1250 MHz (10 Gbps effective) | 1376 MHz (11 Gbps effective) |

| Memory Size | 16 GB | 5 GB |

| Memory Type | GDDR6 | GDDR5X |

| Memory Bus | 256 bit | 320 bit |

| Memory Bandwidth | 320.0 GB/s | 440.3 GB/s |

| Shading Units | 2560 | 3200 |

| TMUs | 160 | 200 |

| ROPs | 64 | 80 |

| RT Cores | 40 | 0 |

| Tensor Cores | 320 | 0 |

| Pixel Rate | 101.8 GPixel/s | 134.6 GPixel/s |

| Texture Rate | 254.4 GTexel/s | 336.6 GTexel/s |

| FP32 Performance | 8.141 TFLOPS | 10.77 TFLOPS |

| FP16 Performance | 16.28 TFLOPS (2:1) | 168.3 GFLOPS (1:64) |

| TDP | 70 W | 250 W |

| Slot Width | Single-slot | Dual-slot |

| Power Connectors | None | 2x 8-pin |

| Suggested PSU | 250 W | 600 W |

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

| Length | 168 mm (6.6 inches) | 267 mm (10.5 inches) |

| DirectX Support | 12 Ultimate (12_2) | 12 (12_1) |

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

| Generation | Tesla Turing (Txx) | Mining GPUs |

| Transistor Density | 25.0M / mm² | 25.1M / mm² |