NVIDIA Quadro K5100M vs NVIDIA Tesla K20c Comparison

Head-to-Head Benchmarks

The only directly comparable benchmark recorded in the database is Geekbench OpenCL. In this test, the NVIDIA Quadro K5100M scores 11,771, while the NVIDIA Tesla K20c scores 11,479. The Quadro K5100M wins this head-to-head by 2.5%. This is a notable result because the Tesla K20c is a much larger, desktop-oriented compute card with significantly more shading units, yet the mobile Quadro part edges it out in raw OpenCL throughput.

Looking at the broader context, both cards sit in the middle of the overall GPU performance distribution. The Tesla K20c holds a 51st percentile ranking among all GPUs, while the Quadro K5100M sits at the 48th percentile. That means the Tesla K20c is slightly better positioned relative to the entire field, even though it loses the direct head-to-head. The difference in percentile is small, but it suggests that the Tesla K20c's overall compute profile is marginally more competitive against a wider range of hardware.

The Tesla K20c's nearest rivals in the database include the AMD Radeon Pro 5500M (average score 11,528, delta -0.4%), the AMD Radeon RX 7800 XT (11,627, -1.3%), the NVIDIA GeForce GTX 1660 (11,680, -1.7%), and the NVIDIA GeForce GTX 780M (11,261, +1.9%). This clustering is tight: the Tesla K20c is within 2% of all four of these cards. The GTX 1660 leads this group, and the Tesla K20c trails it by 1.7%. Against the GTX 780M, the Tesla K20c is ahead by 1.9%, which is a small but meaningful margin.

The Quadro K5100M's nearest rivals are the AMD Radeon R9 M375 (10,070, -0.3%), the AMD Radeon Pro 5300M (10,013, +0.3%), the NVIDIA GeForce GTX 870M (9,959, +0.8%), and the NVIDIA Quadro 6000 (9,846, +2%). Here, the Quadro K5100M's average benchmark score is 10,043, which is the middle of this pack. It is nearly tied with the R9 M375, slightly ahead of the Pro 5300M, and clearly ahead of the GTX 870M and Quadro 6000. The 2% lead over the Quadro 6000 is the largest gap in this group.

The head-to-head result flips the average benchmark picture. The Quadro K5100M has an average benchmark score of 10,043, which is lower than the Tesla K20c's 11,479. Yet in the specific OpenCL test, the Quadro K5100M scores higher. This discrepancy is explained by the fact that the Quadro K5100M's average includes a Metal benchmark score of 8,315, which drags its average down. The Tesla K20c has no Metal result recorded, so its average equals its OpenCL score. When comparing only the common test, the Quadro K5100M is the winner.

Where Each One Wins

The Quadro K5100M wins the only shared benchmark, Geekbench OpenCL, by 2.5%. This is the clear, direct victory. If a workload relies specifically on OpenCL performance, the data shows the Quadro K5100M has a measurable advantage.

The Tesla K20c, however, wins on overall positioning. Its 51st percentile rank places it above the Quadro K5100M's 48th percentile. Additionally, the Tesla K20c's average benchmark score of 11,479 is substantially higher than the Quadro K5100M's 10,043. This is a 14.3% difference in average score. The Tesla K20c also has a higher raw compute ceiling: its FP32 throughput is 3.524 TFLOPS versus 2.369 TFLOPS for the Quadro K5100M. That is a 48.8% advantage in theoretical single-precision compute. For general compute tasks that are not limited to OpenCL, the Tesla K20c is the more powerful card on paper.

The Quadro K5100M wins in efficiency and form factor. Its TDP is 100 W, less than half of the Tesla K20c's 225 W. It is an MXM module, meaning it is designed for portable workstations, while the Tesla K20c is a dual-slot desktop card requiring external power connectors. The Quadro K5100M also has more memory (8 GB versus 5 GB), although its memory bandwidth is lower (115.2 GB/s versus 208.0 GB/s). The extra capacity could help with larger datasets that fit within 8 GB but not 5 GB, even if the transfer speed is slower.

The Tesla K20c wins on memory bandwidth and texture throughput. Its 208.0 GB/s bandwidth is 80.6% higher than the Quadro K5100M's 115.2 GB/s. Its texture rate is 146.8 GTexel/s versus 98.69 GTexel/s, a 48.7% advantage. Pixel rate is also higher: 36.71 GPixel/s versus 24.67 GPixel/s, a 48.8% lead. These metrics favor the Tesla K20c for bandwidth-bound and texture-heavy workloads.

The Verdict

The data points to a clear split. If the workload is specifically OpenCL-based, the Quadro K5100M is the better choice. It wins the head-to-head by 2.5%, and its nearest rivals show it is competitive within a tight cluster. The 8 GB memory capacity is also an advantage for larger working sets.

If the workload is broader, or if raw compute power is the priority, the Tesla K20c is the stronger card. Its 3.524 TFLOPS FP32 throughput is nearly 50% higher than the Quadro K5100M's 2.369 TFLOPS. Its average benchmark score is 14.3% higher. Its 51st percentile rank is above the Quadro K5100M's 48th. The Tesla K20c also has superior memory bandwidth, texture rate, and pixel rate.

The Quadro K5100M is the pick for portable workstations where power draw matters. Its 100 W TDP is less than half of the Tesla K20c's 225 W, and it requires no external power connectors. The Tesla K20c needs a 550 W suggested PSU and a dual-slot footprint, which limits its deployment to desktop systems.

There is no universal winner. The Quadro K5100M wins the only direct benchmark, but the Tesla K20c wins on almost every other measured specification. Choose the Quadro K5100M for OpenCL tasks and low-power mobile systems. Choose the Tesla K20c for maximum compute throughput in a desktop environment.

FAQ

Q: Which card wins in the Geekbench OpenCL test?

A: The NVIDIA Quadro K5100M scores 11,771, beating the NVIDIA Tesla K20c's 11,479 by 2.5%.

Q: Why does the Tesla K20c have a higher average benchmark score if it loses the head-to-head?

A: The Tesla K20c's average benchmark score is 11,479, equal to its OpenCL score. The Quadro K5100M's average is 10,043, which includes a Metal score of 8,315 that lowers its average. The Tesla K20c has no Metal score recorded.

Q: Which card has more shading units?

A: The Tesla K20c has 2,496 shading units, while the Quadro K5100M has 1,536. The Tesla K20c also has 208 TMUs and 40 ROPs, compared to 128 TMUs and 32 ROPs on the Quadro K5100M.

Q: How does memory capacity compare?

A: The Quadro K5100M has 8 GB of GDDR5 memory, while the Tesla K20c has 5 GB. However, the Tesla K20c has a wider 320-bit bus and higher bandwidth at 208.0 GB/s, versus 256-bit and 115.2 GB/s for the Quadro K5100M.

Q: What is the power draw difference?

A: The Tesla K20c has a TDP of 225 W and requires a 1x 6-pin and 1x 8-pin power connector, with a suggested PSU of 550 W. The Quadro K5100M has a TDP of 100 W and uses no external power connectors.

Q: Which card is newer?

A: The Quadro K5100M was released later, on 2013-07-22, while the Tesla K20c was released on 2012-11-11. Both are now end-of-life products.

Architecture Differences

Both cards use the Kepler architecture, but they are built on different chips. The Tesla K20c uses the GK110 chip, while the Quadro K5100M uses the GK104. This is a fundamental difference: GK110 is a larger, more compute-oriented die, while GK104 is a smaller, more balanced chip.

The Tesla K20c is fabricated on a 28 nm process at TSMC, with 7,080 million transistors on a 561 mm² die. The transistor density is 12.6M per mm². The Quadro K5100M is also 28 nm at TSMC, but it packs 3,540 million transistors on a 294 mm² die, for a density of 12.0M per mm². The Tesla K20c has exactly twice the transistor count and nearly twice the die area.

The generation labels differ: the Tesla K20c belongs to the Tesla Kepler (Kxx) generation, while the Quadro K5100M belongs to the Quadro Kepler-M (Kx100M) generation. Their predecessors and successors also differ: the Tesla K20c follows Tesla Fermi and leads to Tesla Maxwell, while the Quadro K5100M follows Quadro Fermi-M and leads to Quadro Maxwell-M.

Both cards support the same API levels: DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175. Neither card has ray tracing cores or tensor cores. Both have no FP16 support recorded.

The Tesla K20c has no display outputs, making it a pure compute accelerator. The Quadro K5100M's display outputs are listed as "Portable Device Dependent," meaning it relies on the host laptop for display connectivity.

Specification Differences

The most obvious difference is physical form. The Tesla K20c is a dual-slot desktop card, 267 mm long, requiring external power. The Quadro K5100M is an MXM module with no power connectors, designed for laptop integration.

Clock speeds differ significantly. The Tesla K20c has no base or boost clock listed, while the Quadro K5100M has a base and boost clock of 771 MHz. Memory clocks also differ: the Tesla K20c runs at 1300 MHz (5.2 Gbps effective), while the Quadro K5100M runs at 900 MHz (3.6 Gbps effective).

Memory configuration is different in every aspect: size (5 GB versus 8 GB), bus width (320-bit versus 256-bit), and bandwidth (208.0 GB/s versus 115.2 GB/s). The Tesla K20c has the faster memory subsystem, but the Quadro K5100M has more capacity.

Compute resources differ substantially. The Tesla K20c has 2,496 shading units, 208 TMUs, and 40 ROPs. The Quadro K5100M has 1,536 shading units, 128 TMUs, and 32 ROPs. Pixel rate is 36.71 GPixel/s for the Tesla K20c versus 24.67 GPixel/s for the Quadro K5100M. Texture rate is 146.8 GTexel/s versus 98.69 GTexel/s. FP32 throughput is 3.524 TFLOPS versus 2.369 TFLOPS.

Power and interface also differ. The Tesla K20c has a 225 W TDP, uses PCIe 2.0 x16, and has a suggested PSU of 550 W. The Quadro K5100M has a 100 W TDP, uses MXM-B (3.0), and has no suggested PSU listed. The Tesla K20c was released earlier and had a launch MSRP of 3,199 USD. The Quadro K5100M has no launch MSRP recorded.