NVIDIA Quadro P6000 vs NVIDIA Tesla T4 Comparison

The NVIDIA Quadro P6000 and NVIDIA Tesla T4 are both end-of-life server and workstation accelerators, but they occupy opposite ends of the design spectrum. The P6000, a Pascal-generation board from 2016, pairs a massive 24 GB GDDR5X frame buffer with 3840 shading units, while the T4, a Turing-generation part from 2018, trades raw shading power for tensor cores, RT cores, and a drastically lower 70 W power draw. Despite these differences, their average benchmark scores are nearly identical: the P6000 averages 67320 points across its two Geekbench tests, and the T4 sits at 66733 points, a mere 0.9% gap. The data, however, reveals a clear split in workload preferences: the P6000 leads in OpenCL, the T4 wins in Vulkan, and each card's architectural strengths point to different use cases.

Head-to-Head Benchmarks

The two available Geekbench tests tell a story of divergent optimizations. In the OpenCL compute test, the Quadro P6000 scores 63852, beating the Tesla T4's 61276 by a decisive 4.2%. This is the largest margin between the two cards in any metric, and it reflects the P6000's higher raw FP32 throughput (12.63 TFLOPS) and wider 384-bit memory bus with 432.8 GB/s bandwidth. The T4, by contrast, manages only 8.141 TFLOPS FP32 and 320.0 GB/s, so its OpenCL deficit is unsurprising. Yet in Vulkan, the tables turn: the T4 posts 72190 points, edging out the P6000's 70788 by 1.9%. This Vulkan advantage likely stems from the T4's Turing architecture, which includes dedicated hardware for async compute and a more modern pipeline, even though it has fewer shading units (2560 vs 3840) and lower memory bandwidth.

When looking at the overall average benchmark score, the P6000 retains a slim lead: 67320 vs 66733, a 0.9% difference. The P6000 also sits at the 92nd percentile among all GPUs, while the T4 is at the 91st. In the context of their nearest rivals, the P6000 is 0.3% ahead of the AMD Radeon Pro Vega 56, 1.3% ahead of the GeForce RTX 4090, and 1.8% ahead of the Tesla P40. The T4, meanwhile, is 0.4% ahead of the RTX 4090, 0.9% behind the P6000, 0.5% behind the Pro Vega 56, and 0.9% ahead of the Tesla P40. These deltas show that both cards are clustered within a narrow performance band, but the P6000's higher raw compute gives it a slight edge in aggregate.

FAQ

Q: Which card has more memory bandwidth?

A: The Quadro P6000 offers 432.8 GB/s of bandwidth from its 384-bit GDDR5X interface, while the Tesla T4 provides 320.0 GB/s over a 256-bit GDDR6 bus. The P6000's bandwidth advantage is 112.8 GB/s, or about 35% higher.

Q: Does the Tesla T4 support ray tracing or tensor operations?

A: Yes. The T4 includes 40 RT cores and 320 tensor cores, making it capable of hardware-accelerated ray tracing and tensor-based deep learning workloads. The Quadro P6000 has no RT or tensor cores, relying entirely on its 3840 shading units for compute.

Q: What is the power consumption difference?

A: The T4 is rated at 70 W TDP and requires no external power connectors, while the P6000 draws 250 W and needs a single 8-pin connector. The suggested PSU for a system with the T4 is 250 W, compared to 600 W for the P6000.

Q: Which card has display outputs?

A: The Quadro P6000 includes 1x DVI and 4x DisplayPort 1.4a outputs, making it suitable for workstation display tasks. The Tesla T4 has no display outputs at all, as it is designed purely for server-side compute and inference.

Q: What is the FP16 performance difference?

A: The T4 delivers 65.13 TFLOPS of FP16 performance (8:1 ratio) thanks to its tensor cores, while the P6000 manages only 197.4 GFLOPS (1:64 ratio). The T4's FP16 throughput is over 300 times higher, a critical gap for AI inference and mixed-precision workloads.

Q: Which card is physically smaller?

A: The T4 is a single-slot card with a length of 168 mm (6.6 inches), while the P6000 is dual-slot and 267 mm (10.5 inches) long. The T4 also has no power connectors, simplifying installation in dense servers.

The Verdict

Choose the Quadro P6000 if your priority is raw FP32 compute, large memory capacity, or display output. Its 24 GB GDDR5X frame buffer and 432.8 GB/s bandwidth are unmatched by the T4, and its OpenCL score is 4.2% higher. The P6000 also has a slightly higher average benchmark score (67320 vs 66733) and a 0.9% overall advantage. For workstation rendering, CAD, or any task that benefits from 12.63 TFLOPS of FP32 and a 384-bit memory path, the P6000 is the stronger choice, despite its 250 W TDP and dual-slot footprint.

Choose the Tesla T4 if you need low power, high FP16 throughput, or tensor-accelerated inference. Its 70 W TDP and single-slot design make it ideal for dense server deployments, and its 65.13 TFLOPS FP16 performance is a massive advantage for deep learning. The T4 also wins the Vulkan benchmark by 1.9%, indicating better modern API utilization. While it has only 16 GB of memory and no display outputs, its tensor cores and RT cores provide capabilities the P6000 lacks entirely. For AI inference, mixed-precision training, or ray-traced compute in a headless environment, the T4 is the clear pick.

Specification Differences

| Specification | Quadro P6000 | Tesla T4 |

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

| Memory size | 24 GB | 16 GB |

| Memory type | GDDR5X | GDDR6 |

| Memory bus width | 384 bit | 256 bit |

| Memory bandwidth | 432.8 GB/s | 320.0 GB/s |

| Base clock | 1506 MHz | 585 MHz |

| Boost clock | 1645 MHz | 1590 MHz |

| Effective memory clock | 9 Gbps | 10 Gbps |

| Shading units | 3840 | 2560 |

| TMUs | 240 | 160 |

| ROPs | 96 | 64 |

| RT cores | None | 40 |

| Tensor cores | None | 320 |

| FP32 performance | 12.63 TFLOPS | 8.141 TFLOPS |

| FP16 performance | 197.4 GFLOPS (1:64) | 65.13 TFLOPS (8:1) |

| TDP | 250 W | 70 W |

| Slot width | Dual-slot | Single-slot |

| Power connectors | 1x 8-pin | None |

| Suggested PSU | 600 W | 250 W |

| Display outputs | 1x DVI, 4x DisplayPort 1.4a | No outputs |

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

| Process node | 16 nm | 12 nm |

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

| Die size | 471 mm² | 545 mm² |

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

| Release date | 2016-09-30 | 2018-09-12 |

| Launch MSRP | 5,999 USD | Not listed |

Architecture Differences

The two cards represent different architectural generations. The Quadro P6000 uses the GP102 chip built on TSMC's 16 nm process, packing 11,800 million transistors into a 471 mm² die. Its Pascal architecture lacks dedicated tensor or RT cores, and its FP16 throughput is a minuscule 197.4 GFLOPS at a 1:64 ratio, meaning it is heavily optimized for FP32 compute. In contrast, the Tesla T4 is based on the TU104 chip on TSMC's 12 nm process, with 13,600 million transistors on a 545 mm² die. Turing introduces 320 tensor cores and 40 RT cores, enabling FP16 at 65.13 TFLOPS (8:1 ratio) and hardware ray tracing. The T4 also supports DirectX 12 Ultimate (12_2), while the P6000 only reaches DirectX 12 (12_1). The transistor density is nearly identical (25.1M / mm² for P6000 vs 25.0M / mm² for T4), but the T4's larger die and newer node allow for specialized hardware. The T4 also has a much lower base clock (585 MHz vs 1506 MHz) but a boost clock close to the P6000 (1590 vs 1645 MHz), indicating a design tuned for sustained, power-efficient operation rather than peak frequency.

Where Each One Wins

Quadro P6000 wins when:

• Raw FP32 compute is required: its 12.63 TFLOPS and 4.2% OpenCL advantage over the T4 make it better for general-purpose GPU computing.
• Memory capacity and bandwidth matter: 24 GB at 432.8 GB/s vs 16 GB at 320.0 GB/s gives it a clear edge for large datasets or high-resolution textures.
• Display output is needed: with DVI and four DisplayPort 1.4a connectors, it can drive multiple monitors directly.
• Workloads rely on the older but proven Pascal pipeline, especially in legacy OpenCL applications.

Tesla T4 wins when:

• Power efficiency is critical: its 70 W TDP and lack of power connectors allow for dense server configurations, while the P6000 needs 250 W and a 600 W PSU.
• FP16 or tensor operations are involved: 65.13 TFLOPS FP16 and 320 tensor cores make it far superior for AI inference and mixed-precision training.
• Vulkan is the target API: its 1.9% advantage in the Vulkan benchmark suggests better modern API performance.
• Ray tracing is needed: 40 RT cores enable hardware-accelerated ray tracing, which the P6000 cannot do.
• Space is limited: the T4's single-slot, 168 mm length is less than two-thirds the P6000's size.