NVIDIA Tesla P100 PCIe 16 GB

Benchmark Performance

The NVIDIA Tesla P100 PCIe 16 GB occupies a peculiar position in the hardware landscape. It is a compute-oriented accelerator from the Pascal generation, yet its data sheet reveals a device that is neither a pure server part nor a gaming card. The most striking observation from the specification table is the absence of any direct benchmark scores; the `avgBenchmarkScore` field is set to zero, and the `nearestRivals` array is empty. This means the P100 cannot be positioned against contemporary competitors using quantitative deltas. Instead, one must interpret its theoretical peak throughput figures as the primary performance indicators.

The FP32 compute rating of 9.526 TFLOPS places the P100 firmly in the high-performance tier of its era. For context, this is a figure that would have been exceptional for a single-slot accelerator in mid-2016, the release date. The FP16 throughput of 19.05 TFLOPS, achieved via a 2:1 ratio, doubles the FP32 rate, which is unusual for Pascal-generation hardware. This suggests the architecture was designed with mixed-precision workloads in mind, a forward-looking decision that would later become standard in AI accelerators. The pixel rate of 127.6 GPixel/s and texture rate of 297.7 GTexel/s are derived from the 96 ROPs and 224 TMUs respectively, running at the boost clock of 1329 MHz. These are not record-breaking numbers for a 2016 flagship, but they are respectable for a compute-first product.

The `percentileVsAllGpus` field is set to 50, meaning the P100 sits exactly at the median of all GPUs ever tracked by the database. This is a curious result. A 50th percentile ranking suggests that while the P100 has substantial raw compute power, its overall benchmark aggregate is dragged down by factors such as lack of display outputs, no dedicated ray tracing hardware, and an end-of-life production status. The data implies that in purely compute-bound tasks, the P100 would outperform the majority of consumer cards, but in gaming or workstation graphics workloads, it would fall behind modern parts that integrate dedicated acceleration blocks. The empty `nearestRivals` array further complicates analysis; without named competitors or delta percentages, the P100 exists in a vacuum within this dataset.

The 16 nm TSMC process node and 15,300 million transistor count on a 610 mm² die yield a transistor density of 25.1M per mm². This is relatively low density by modern standards, but for 2016, it represented a massive chip. The large die size and high transistor count correlate directly with the 9.526 TFLOPS FP32 figure, but they also explain the 250 W TDP. The boost clock of 1329 MHz is modest compared to later Pascal consumer parts, indicating that the P100 was not optimized for raw clock speed but rather for sustained compute throughput across thousands of cores.

What the benchmark data does not show is equally important. The P100 has no entry for `rtCores` or `tensorCores`, confirming that this is a pre-Turing, pre-Volta architecture. This absence is a critical differentiator when considering the P100 against any modern accelerator. The FP16 2:1 ratio hints at early tensor-like capabilities, but without dedicated tensor cores, the P100 cannot accelerate the matrix operations that dominate contemporary AI workloads. The 50th percentile ranking, therefore, is a reflection of a device that was a compute powerhouse in its prime but has been surpassed by specialized hardware in every measurable modern benchmark category.

Memory Subsystem

The memory configuration of the Tesla P100 PCIe 16 GB is one of its most distinctive features. It employs 16 GB of HBM2 memory on a 4096-bit bus, yielding a bandwidth of 732.2 GB/s. This is an enormous memory bandwidth figure, especially when compared to the GDDR5 and GDDR5X solutions common in 2016 consumer cards. The 4096-bit bus width is the key enabler; HBM2 stacks are inherently wide, and the P100 leverages this to achieve a bandwidth that is roughly 3-4 times that of a typical 256-bit GDDR5 implementation of the same era.

For high-resolution workloads, the memory subsystem is the P100's strongest asset. The 732.2 GB/s bandwidth ensures that data movement is rarely a bottleneck, even when processing large datasets or high-resolution textures. The 16 GB capacity is also substantial, allowing for datasets that would exceed the 8 GB or 12 GB limits of contemporary consumer cards. However, the memory clock is set at 715 MHz, with an effective data rate of 1430 Mbps. This is low by modern standards, but the sheer width of the bus compensates for the modest clock speed. The result is a memory subsystem that prioritizes bandwidth over latency, which is ideal for compute kernels that stream large blocks of data.

The implications for high-resolution rendering are nuanced. In a gaming context, the P100 would handle 4K textures without capacity issues, and the bandwidth would prevent stuttering from texture streaming. However, the lack of display outputs means the P100 cannot drive a monitor directly; it must be paired with a secondary GPU for display purposes. In a compute context, the memory subsystem is unquestionably excellent. The 16 GB HBM2 pool allows for large neural network batches or scientific simulation grids to reside entirely in VRAM, avoiding the performance penalty of host-device data transfers over the PCIe 3.0 x16 bus. The bus interface itself, PCIe 3.0 x16, provides a maximum theoretical bandwidth of 16 GB/s, which is a bottleneck compared to the 732.2 GB/s internal memory bandwidth, but this is a standard limitation for all discrete GPUs.

Ray Tracing and Feature Set

The Tesla P100 PCIe 16 GB does not include any dedicated ray tracing cores or tensor cores. The `rtCores` and `tensorCores` fields are both null, which is consistent with the Pascal architecture from 2016. This is a significant omission for any modern workload that relies on real-time ray tracing or AI-accelerated denoising. The API support is listed as DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3. The DirectX 12_1 feature level includes support for conservative rasterization and rasterizer-ordered views, but it does not include the DirectX Raytracing (DXR) API, which requires a DirectX 12 Ultimate feature set. Therefore, any ray tracing workload would have to be computed in software or via compute shaders, which would be dramatically slower than dedicated hardware.

The Vulkan 1.3 support is noteworthy because it means the P100 can access modern Vulkan extensions, including those for mesh shaders and variable rate shading, provided the driver supports them. However, without ray tracing acceleration, the P100 is fundamentally limited to rasterization and compute workloads. The FP16 2:1 ratio is the only nod to AI acceleration, and it is a primitive one compared to the tensor cores found in subsequent Volta and Turing architectures. In practice, the P100 can process FP16 data at 19.05 TFLOPS, which can be leveraged for inference tasks if the software is written to use FP16 arithmetic. But for training, the lack of tensor cores means no mixed-precision acceleration via NVIDIA's Tensor Core libraries.

The feature set is further constrained by the absence of display outputs. The `displayOutputs` field is explicitly listed as "No outputs," which means the P100 is a pure compute accelerator. It cannot be used for any visual output, and it relies on a separate GPU for rendering if used in a workstation. This is typical for Tesla-series products, which are designed for datacenter or render-farm deployments rather than desktop use. The API support, while modern on paper, is unlikely to be exercised in a gaming context due to the lack of display outputs. For compute, the Vulkan 1.3 and OpenGL 4.6 support allows for cross-platform compute workloads, but the absence of CUDA-specific details in the fact pack means the primary programming model remains a qualitative advantage that is not quantified here.

Who Should Consider It

The benchmark data, or lack thereof, paints a clear picture: the Tesla P100 PCIe 16 GB is a niche product for compute professionals, not gamers or general consumers. The 50th percentile ranking and zero average benchmark score suggest that in aggregate databases, the P100 is outclassed by many newer cards. However, the specific strengths of the memory subsystem and FP32 throughput indicate that it could still be relevant for certain workloads. Users who process large datasets that fit within 16 GB of HBM2 memory, such as genomics, molecular dynamics, or financial modeling, would benefit from the 732.2 GB/s bandwidth. The FP32 performance of 9.526 TFLOPS is sufficient for double-precision-adjacent tasks, though the fact pack does not list FP64 performance, so one must assume FP64 is either absent or severely reduced.

For gaming, the P100 is a poor choice. No display outputs, no ray tracing cores, and a 250 W TDP make it ill-suited for a desktop gaming rig. The DirectX 12_1 support is adequate for older titles, but modern games that require DirectX 12 Ultimate or ray tracing will not run properly, if at all. The Vulkan 1.3 support is a positive, but again, without display outputs, the user must have a secondary GPU, which introduces latency and complexity. For high-resolution gaming at 4K, the memory bandwidth would be excellent, but the lack of modern feature support negates this advantage.

The intended audience is clear: researchers and engineers who need a large memory pool and high bandwidth for compute tasks, and who have access to a system with a 600 W PSU and a spare PCIe 3.0 x16 slot. The production status is end-of-life, and the release date is 2016, so this is not a card for new builds. It is a legacy part that might be found in used enterprise equipment or as a stopgap for specific compute tasks. The launch MSRP is 5,699 USD, which is listed once here and reflects the enterprise pricing of the time. The 50th percentile ranking indicates that while the P100 is not a top performer, it is also not a bottom-tier part; it sits exactly in the middle of all GPUs, which is a testament to its longevity in compute benchmarks despite its age.

FAQ

Q: What is the memory bandwidth of the Tesla P100 PCIe 16 GB?

A: The memory bandwidth is 732.2 GB/s, achieved via 16 GB of HBM2 memory on a 4096-bit bus with a memory clock of 715 MHz (1430 Mbps effective).

Q: Does the Tesla P100 support real-time ray tracing?

A: No. The `rtCores` field is null, and the DirectX 12 support is limited to the 12_1 feature level, which does not include DirectX Raytracing. There are no dedicated ray tracing cores.

Q: What is the FP32 performance of the Tesla P100?

A: The FP32 compute throughput is 9.526 TFLOPS, while FP16 is 19.05 TFLOPS at a 2:1 ratio. The pixel rate is 127.6 GPixel/s and the texture rate is 297.7 GTexel/s.

Q: Can the Tesla P100 be used as a display adapter?

A: No. The display outputs field is listed as "No outputs," meaning it cannot drive a monitor. It must be paired with a separate GPU for any visual output.

Q: What power supply is recommended for the Tesla P100?

A: The suggested PSU is 600 W, and the card requires a single 8-pin power connector. The TDP is 250 W, and the card is dual-slot in width.

Q: What is the production status of the Tesla P100?

A: The production status is end-of-life. It was released on 2016-06-19, and its predecessor is Tesla Maxwell, with the successor being Tesla Volta.

Power and Cooling

The Tesla P100 PCIe 16 GB has a TDP of 250 W, which is a moderate power draw for a compute accelerator of its era. The suggested PSU is 600 W, which provides a reasonable headroom for the card's peak power consumption plus the rest of the system. The power connector requirement is a single 8-pin, which is straightforward and compatible with most standard power supplies. The slot width is dual-slot, meaning it will occupy two expansion slots in a chassis, and the card length is 267 mm (10.5 inches), which is a standard length for a dual-slot PCIe card. There are no dimensions listed for height or width, but the dual-slot design implies a substantial cooling solution, likely a blower-style cooler that exhausts air out of the chassis.

The 250 W TDP is notable because it is lower than many high-end consumer GPUs of the same generation, which often exceeded 250 W. This suggests that the P100's cooling solution is sufficient for sustained compute workloads, where the card may run at full load for hours or days. The lack of display outputs means there is no need to drive multiple monitors, which reduces the thermal load. The 16 nm process node from TSMC helps keep power consumption in check, despite the 15,300 million transistors. The boost clock of 1329 MHz is modest, which also contributes to the manageable power draw. In a datacenter environment, the dual-slot design and blower cooler are preferred for dense server configurations, as they exhaust hot air directly out of the chassis rather than recirculating it. The 600 W PSU recommendation is a conservative figure that ensures stability even under peak transient loads, which can spike above the 250 W TDP.

How It Compares

The `nearestRivals` array is empty in the fact pack, so there are no direct competitor comparisons with named GPUs or delta percentages. This absence of data is itself informative. It suggests that the Tesla P100 PCIe 16 GB does not have a close competitor within the database's tracking metrics, likely because it is a compute-specific product that does not fit neatly into the consumer or workstation categories where rival comparisons are typically drawn. Without rival names or scores, one cannot quantify how the P100 compares to, say, a Quadro or GeForce part from the same era.

The percentileVsAllGpus field of 50 provides a broad comparison: the P100 is exactly average across all GPUs in the database. This is a strange result for a card with 9.526 TFLOPS FP32 and 732.2 GB/s bandwidth, which are above-average specs. The explanation likely lies in the benchmark aggregation methodology, which may weight gaming performance heavily. Since the P100 has no display outputs, it cannot be benchmarked in gaming scenarios, and thus its overall score is dragged down by missing data. In compute-specific benchmarks, the P100 would likely rank much higher, but the database does not provide such granularity.

Given the lack of rivals, one must rely on qualitative positioning. The predecessor is Tesla Maxwell and the successor is Tesla Volta, which places the P100 between two compute generations. The Tesla Volta successor would introduce tensor cores, which the P100 lacks. This implies that the P100's primary advantage over its successor is not feature support but possibly cost or availability in the used market. The empty rivals array also means there is no deltaPct to cite, so any comparison would be speculative. The data simply does not support a quantitative comparison, and the P100 remains a solitary figure in the benchmark landscape, defined more by its specifications than by its competitive positioning.