NVIDIA Tesla P100 PCIe 12 GB

The NVIDIA Tesla P100 PCIe 12 GB is a workstation accelerator built on the Pascal architecture, designed for compute-heavy tasks rather than traditional gaming. As an end-of-life product with a 50th percentile ranking among all GPUs, its performance profile is defined by its massive compute throughput and high-bandwidth memory, making it a specialized tool with distinct strengths and limitations.

Benchmark Performance

The Tesla P100’s compute capabilities are its defining feature, with the data showing 9.526 TFLOPS of FP32 performance and 19.05 TFLOPS of FP16 performance at a 2:1 ratio. This FP16 figure is particularly notable, as it represents double the FP32 throughput, a design choice that accelerates machine learning workloads which rely heavily on half-precision arithmetic. The 3584 shading units, combined with 224 texture mapping units and 96 ROPs, deliver a texture fill rate of 297.7 GTexel/s and a pixel fill rate of 127.6 GPixel/s, figures that reflect a card engineered for parallel processing rather than rasterization efficiency.

The benchmark data places this card at the 50th percentile among all GPUs, indicating it sits exactly at the median of the performance distribution. This positioning is somewhat paradoxical: the card’s raw compute numbers are impressive, but its lack of modern features and specialized gaming optimizations means its real-world performance in conventional benchmarks is middling. The clock speeds of 1190 MHz base and 1329 MHz boost are modest by today’s standards, suggesting that the card’s performance comes from its sheer number of cores rather than high frequency operation. In compute-heavy tasks, the FP32 and FP16 figures would place it ahead of many consumer cards of its era, but the absence of rival comparison data in the fact pack means these numbers stand alone as indicators of theoretical peak throughput rather than competitive positioning.

Ray Tracing and Feature Set

The Tesla P100 predates the ray tracing era, and the data confirms this: there are no ray tracing cores and no tensor cores listed in its specifications. This is a fundamental architectural limitation, as the Pascal generation lacks the dedicated hardware that later NVIDIA cards use for real-time ray tracing and AI-accelerated features. The card’s API support includes DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3, which means it can run modern graphics APIs, but without the specialized cores, any ray tracing workload would have to be handled by the general-purpose shaders, resulting in severely reduced performance.

The absence of tensor cores is particularly relevant for machine learning inference and training, where these cores provide massive speedups for matrix operations. The FP16 2:1 ratio partially compensates for this, allowing the card to process half-precision data at double the rate of FP32, but it cannot match the efficiency of dedicated tensor core hardware. The feature set is therefore best understood as a bridge between the compute-focused Tesla Maxwell generation that preceded it and the tensor core-equipped Tesla Volta that followed. For users requiring ray tracing or tensor core acceleration, this card is clearly not the right choice, but for pure compute tasks that leverage FP32 or FP16 arithmetic, the feature set is adequate, if dated.

Memory Subsystem

The memory configuration is where the Tesla P100 shows its most impressive specifications. It features 12 GB of HBM2 memory on a 3072-bit bus, delivering a bandwidth of 549.1 GB/s. This is an enormous memory bandwidth figure, far exceeding what most consumer graphics cards of any generation offer, and it is the key to the card’s compute performance. The memory clock runs at 715 MHz, with an effective data rate of 1430 Mbps, which when multiplied across the 3072-bit bus produces the stated bandwidth.

For high-resolution workloads, this memory subsystem is a significant advantage. The 12 GB capacity allows for large datasets to be held entirely in VRAM, avoiding the performance penalty of data transfers over the PCIe 3.0 x16 interface. The 549.1 GB/s bandwidth ensures that the compute units are fed with data at a rate that approaches their processing capability, minimizing stalls. In the context of the card’s 50th percentile overall ranking, the memory subsystem is likely a primary contributor to its performance in memory-bound tasks, such as large matrix multiplications or scientific simulations. However, for gaming at high resolutions, the bandwidth is less relevant than the card’s raw shader performance, which is not optimized for the rasterization-heavy workloads typical of games.

How It Compares

The fact pack provides no nearest rival data for this card, which limits direct comparisons. However, its position as a predecessor to Tesla Volta and a successor to Tesla Maxwell gives some context. Against its Maxwell predecessors, the Pascal architecture’s move to HBM2 memory and increased FP16 throughput represents a clear generational leap in compute capability, particularly for workloads that benefit from the 2:1 FP16 ratio. The 16 nm process node from TSMC, with 15,300 million transistors on a 610 mm² die, yields a transistor density of 25.1M per mm², a figure that was advanced for its time.

Compared to its Volta successor, the P100 lacks the tensor cores that define Volta’s AI capabilities, but its FP32 and FP16 performance are still respectable in absolute terms. The 50th percentile ranking suggests that in a modern context, it performs at the median of all GPUs, which means it is outclassed by many newer mid-range and high-end cards in terms of raw gaming performance. Its strengths lie specifically in compute workloads where its memory bandwidth and FP16 throughput can be fully utilized. Without rival scores to cite, the analysis must rely on the architecture’s known characteristics: the card is a compute specialist, not a general-purpose performer.

Power and Cooling

The Tesla P100 has a TDP of 250 W, a figure that is moderate for a card of its compute capability. The suggested PSU is 600 W, which provides a reasonable margin for the card’s power draw plus system components. Power is delivered via a single 8-pin connector, which is a standard configuration that most power supplies support. The card is dual-slot in width, and its length is 267 mm or 10.5 inches, making it a substantial physical presence that requires adequate clearance in a chassis.

The 250 W TDP is notable in that it allows the card to achieve its compute performance without requiring the more complex power delivery systems of higher-end cards. The dual-slot cooler is designed to dissipate this heat effectively in a workstation environment, where sustained compute loads are common. For users integrating this card into a system, the 600 W PSU recommendation provides ample headroom, and the single 8-pin connector simplifies installation compared to cards requiring multiple connectors. The card has no display outputs, which reinforces its role as a compute accelerator rather than a graphics card, and means it must be paired with a separate GPU for any display functionality.

Who Should Consider It

The Tesla P100 is for users whose workloads align with its compute-focused design. The 9.526 TFLOPS FP32 performance and 19.05 TFLOPS FP16 performance make it suitable for scientific computing, data analysis, and machine learning training and inference, provided the software can leverage the 2:1 FP16 ratio. The 12 GB HBM2 memory with 549.1 GB/s bandwidth is ideal for handling large datasets that exceed the capacity of consumer cards, and the memory bandwidth ensures that compute units are not starved for data. For tasks like numerical simulations, finite element analysis, or deep learning model training, this card can deliver strong performance.

However, the 50th percentile ranking and lack of ray tracing and tensor cores make it a poor choice for gaming or modern graphics workloads. Its DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3 support mean it can run games, but its performance would be mediocre by current standards, and its lack of display outputs requires a separate GPU for output. The card is best suited for compute servers or workstations where its capabilities can be fully utilized in a headless configuration. The 250 W TDP and 600 W PSU requirement are manageable, and the dual-slot design is standard. Ultimately, this card is a specialized tool for a specific audience, and its value is determined entirely by the user’s compute needs, not by general-purpose performance metrics.

FAQ

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

A: The Tesla P100 delivers 9.526 TFLOPS of FP32 performance, which is its single-precision compute throughput.

Q: Does the Tesla P100 support ray tracing?

A: No, the Tesla P100 does not have ray tracing cores, and its Pascal architecture does not include dedicated hardware for ray tracing workloads.

Q: What memory type and bandwidth does the Tesla P100 use?

A: The card uses 12 GB of HBM2 memory with a 3072-bit bus, providing a bandwidth of 549.1 GB/s.

Q: What is the FP16 performance of the Tesla P100?

A: The Tesla P100 achieves 19.05 TFLOPS of FP16 performance, which is double its FP32 throughput at a 2:1 ratio.

Q: What is the power consumption and PSU requirement for the Tesla P100?

A: The Tesla P100 has a TDP of 250 W and requires a suggested PSU of 600 W, with power delivered via a single 8-pin connector.

Q: What APIs does the Tesla P100 support?

A: The card supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3, though it has no display outputs.