NVIDIA Quadro GV100 vs NVIDIA Tesla P4 Comparison

The NVIDIA Tesla P4 and NVIDIA Quadro GV100 represent two very different eras and purposes within NVIDIA’s professional lineup, despite both being end-of-life products. The Tesla P4 is a compact, low-power Pascal card built for efficient inference and datacenter tasks, while the Quadro GV100 is a massive Volta workstation powerhouse designed for heavy compute and visualization. Benchmark data shows a stark performance chasm between them, with the GV100 dominating in raw compute while the P4 offers a far more accessible physical footprint. The following analysis breaks down their architectural differences, specification gaps, and benchmark results to help determine which card fits specific workloads.

Architecture Differences

The foundational architecture separates these two GPUs completely. The Tesla P4 uses the GP104 chip built on Pascal architecture, manufactured on a 16 nm process at TSMC. The Quadro GV100 uses the GV100 chip built on Volta architecture, manufactured on a 12 nm process, also at TSMC. The transistor counts reflect the scale difference: the P4 contains 7,200 million transistors on a 314 mm² die, while the GV100 packs 21,100 million transistors onto a massive 815 mm² die. The transistor density is higher on the GV100 at 25.9M / mm² versus 22.9M / mm² for the P4, showing Volta’s more advanced design.

The most significant architectural feature is the tensor cores present only on the GV100. It includes 640 tensor cores, which are dedicated hardware for matrix math and deep learning operations. The P4 has no tensor cores at all. This is a fundamental difference in compute capability: the GV100 can accelerate AI workloads far beyond what the P4 can achieve. The shading units also differ drastically, with the P4 featuring 2,560 shading units, while the GV100 has 5,120. The GV100 also doubles the texture mapping units (TMUs) at 320 versus 160, and the render output units (ROPs) at 128 versus 64.

Memory architecture is another major divergence. The P4 uses GDDR5 memory on a 256-bit bus, while the GV100 uses HBM2 on a 4096-bit bus. This is not just a capacity difference; the memory type and bus width fundamentally alter how data flows through the chip. The P4’s memory runs at 1502 MHz (effective 6 Gbps), while the GV100’s runs at 848 MHz (effective 1696 Mbps). Despite the lower clock speed, the GV100’s enormous bus width gives it vastly more bandwidth. The physical design also differs: the P4 is a single-slot card with no power connectors, while the GV100 is a dual-slot card requiring a 1x 8-pin power connector. The P4 has no display outputs, making it purely a compute accelerator, while the GV100 has 4x DisplayPort 1.4a outputs for direct display connection.

FAQ

Q: Which card has more memory and bandwidth?

A: The Quadro GV100 has 32 GB of HBM2 memory with 868.4 GB/s bandwidth, while the Tesla P4 has 8 GB of GDDR5 with 192.3 GB/s bandwidth. The GV100’s 4096-bit bus versus the P4’s 256-bit bus drives this massive bandwidth advantage.

Q: Does the Quadro GV100 support tensor operations?

A: Yes, the GV100 includes 640 tensor cores, which are specialized for deep learning and matrix math. The Tesla P4 has no tensor cores, so it cannot accelerate these workloads in the same way.

Q: What are the power requirements for each card?

A: The Tesla P4 has a 75 W TDP with no power connectors and a suggested PSU of 250 W. The Quadro GV100 has a 250 W TDP, requires a 1x 8-pin power connector, and needs a 600 W suggested PSU.

Q: Can either card output video to a display?

A: Only the Quadro GV100 can output video directly, with 4x DisplayPort 1.4a outputs. The Tesla P4 has no outputs, making it strictly a compute or inference card.

Q: Which card has a higher boost clock?

A: The Quadro GV100 has a higher boost clock at 1627 MHz versus the Tesla P4’s 1114 MHz. The GV100 also has a higher base clock at 1132 MHz versus 886 MHz.

Q: How do their average benchmark scores compare?

A: The Tesla P4 has an average benchmark score of 37,628, while the Quadro GV100 has an average of 35,520. Despite the GV100 winning the head-to-head tests, its average is pulled down by low DirectX scores, while the P4’s two scores are both high.

Specification Differences

The specifications that differ between the two cards are extensive and define their performance envelopes.

• Process Node: 16 nm (P4) vs 12 nm (GV100)
• Transistors: 7,200 million vs 21,100 million
• Die Size: 314 mm² vs 815 mm²
• Transistor Density: 22.9M / mm² vs 25.9M / mm²
• Base Clock: 886 MHz vs 1132 MHz
• Boost Clock: 1114 MHz vs 1627 MHz
• Memory Clock: 1502 MHz (6 Gbps effective) vs 848 MHz (1696 Mbps effective)
• Memory Size: 8 GB vs 32 GB
• Memory Type: GDDR5 vs HBM2
• Memory Bus Width: 256 bit vs 4096 bit
• Memory Bandwidth: 192.3 GB/s vs 868.4 GB/s
• Shading Units: 2560 vs 5120
• TMUs: 160 vs 320
• ROPs: 64 vs 128
• Tensor Cores: None vs 640
• Pixel Rate: 71.30 GPixel/s vs 208.3 GPixel/s
• Texture Rate: 178.2 GTexel/s vs 520.6 GTexel/s
• FP32 Performance: 5.704 TFLOPS vs 16.66 TFLOPS
• FP16 Performance: 89.12 GFLOPS (1:64) vs 33.32 TFLOPS (2:1)
• TDP: 75 W vs 250 W
• Slot Width: Single-slot vs Dual-slot
• Power Connectors: None vs 1x 8-pin
• Suggested PSU: 250 W vs 600 W
• Display Outputs: No outputs vs 4x DisplayPort 1.4a
• Length: 168 mm (6.6 inches) vs 267 mm (10.5 inches)
• Height: Not listed vs 111 mm (4.4 inches)

Head-to-Head Benchmarks

The direct benchmark comparison between these two cards is one-sided but revealing. In Geekbench OpenCL, the Quadro GV100 scores 150,004 versus the Tesla P4’s 34,947. This is a -76.7% delta for the P4, meaning the GV100 is over four times faster in this compute-heavy test. The OpenCL test exercises raw compute throughput, where the GV100’s 5,120 shading units and 16.66 TFLOPS FP32 performance crush the P4’s 2,560 units and 5.704 TFLOPS.

In Geekbench Vulkan, the GV100 again wins decisively with a score of 139,526 versus the P4’s 40,309, a delta of -71.1%. Vulkan is more graphics-oriented, but the GV100’s superior pixel rate (208.3 GPixel/s versus 71.30 GPixel/s) and texture rate (520.6 GTexel/s versus 178.2 GTexel/s) ensure it dominates here as well. The P4 does not win any of the head-to-head tests; the data shows 0 wins for the P4 and 2 wins for the GV100.

However, the average benchmark scores tell a more nuanced story. The P4’s average is 37,628, which is actually higher than the GV100’s average of 35,520. This is because the GV100’s Passmark DirectX scores are extremely low: 140 on DirectX 10, 168 on DirectX 11, 84 on DirectX 12, and 207 on DirectX 9. These scores drag down its overall average, while the P4’s two Geekbench scores are both above 34,000. The GV100 does score well on Passmark G3D (19,650) and GPU Compute (9,069), but the DirectX numbers are anomalously low for a professional card.

Where Each One Wins

The Quadro GV100 wins decisively in raw compute and memory bandwidth. Its 868.4 GB/s bandwidth and 32 GB of HBM2 memory make it ideal for large datasets, scientific simulations, and deep learning training where tensor cores are essential. The 33.32 TFLOPS FP16 performance is a massive advantage for AI inference and training, dwarfing the P4’s 89.12 GFLOPS FP16 (at a 1:64 ratio). The GV100 also wins for any workload requiring display output, thanks to its 4x DisplayPort 1.4a outputs, making it suitable for visualization and workstation use.

The Tesla P4 wins in power efficiency and physical form factor. Its 75 W TDP and single-slot design mean it can be installed in dense servers without additional power cabling, with a suggested PSU of only 250 W. The P4’s 168 mm length is significantly shorter than the GV100’s 267 mm, allowing installation in smaller chassis. For tasks that do not require tensor cores or massive memory, such as lightweight inference or basic compute, the P4’s lower power draw is a practical advantage. The P4’s higher average benchmark score (37,628 vs 35,520) also suggests it is more consistent in general compute tasks, despite losing the head-to-head tests.

The Verdict

The data points to a clear split: the Quadro GV100 is the superior card for any workload that can leverage its massive compute resources. If the task involves 32 GB of memory, 868.4 GB/s bandwidth, or 640 tensor cores, the GV100 is the only choice. Its performance in Geekbench OpenCL (150,004) and Vulkan (139,526) shows it is over 3.7x and 3.4x faster than the P4 respectively. For AI, scientific computing, and high-end visualization, the GV100’s 16.66 TFLOPS FP32 and 33.32 TFLOPS FP16 are unmatched by the P4.

The Tesla P4 is the card for constrained environments. Its 75 W TDP and single-slot design make it far easier to deploy in power-limited or space-limited servers. For basic compute tasks, its average benchmark score of 37,628 is actually higher than the GV100’s 35,520, indicating more consistent performance across the board. The P4’s 8 GB of GDDR5 memory is sufficient for many inference tasks, and its lack of display outputs is not a drawback in a headless server.

Choose the Quadro GV100 for maximum compute, memory capacity, and AI capability. Choose the Tesla P4 for efficiency, compactness, and simplicity in low-power deployments. The GV100’s launch MSRP of 8,999 USD reflects its workstation-class positioning, while the P4’s lack of a listed MSRP suggests it was sold through OEM channels. The benchmark results are unambiguous: the GV100 is the performance king, but the P4 is the practical choice for specific, power-conscious scenarios.