NVIDIA Quadro K4200 vs NVIDIA Tesla K20c Comparison

The NVIDIA Quadro K4200 and NVIDIA Tesla K20c are both end-of-life Kepler-generation professional cards, but they target fundamentally different workloads. The data shows a clear split: the Quadro K4200 is the better general-purpose compute card in the OpenCL benchmark, while the Tesla K20c offers superior raw hardware specifications for compute-heavy tasks. The Quadro K4200 wins the only head-to-head benchmark, but the Tesla K20c’s larger memory pool and higher shader count make it the more capable card for specific professional compute scenarios.

Where Each One Wins

The Quadro K4200 takes the single measured benchmark victory. In the Geekbench OpenCL test, the Quadro K4200 scores 12,313 points against the Tesla K20c’s 11,479 points, a 7.3% advantage. This win places the Quadro K4200 in the 52nd percentile of all GPUs, while the Tesla K20c sits just below at the 51st percentile. The Quadro K4200’s average benchmark score of 12,398 also exceeds the Tesla K20c’s average of 11,479, reinforcing its edge in this specific synthetic workload.

The Tesla K20c, however, wins on architectural capability. It features 2,496 shading units versus the Quadro K4200’s 1,344, giving it nearly double the compute cores. Its 208 texture mapping units and 40 render output units also outclass the Quadro K4200’s 112 TMUs and 32 ROPs. In raw throughput terms, the Tesla K20c delivers 3.524 TFLOPS of FP32 performance and a texture rate of 146.8 GTexel/s, compared to the Quadro K4200’s 2.107 TFLOPS and 87.81 GTexel/s. The Tesla K20c also offers 5 GB of GDDR5 memory on a 320-bit bus, yielding 208.0 GB/s of bandwidth, versus the Quadro K4200’s 4 GB on a 256-bit bus at 172.8 GB/s.

The practical interpretation is that the Quadro K4200 wins in the OpenCL compute benchmark, but the Tesla K20c’s hardware specifications suggest it would excel in workloads that scale with shader count and memory bandwidth. The data shows a 67.2% advantage in FP32 throughput for the Tesla K20c, and a 20.4% advantage in memory bandwidth, which are significant for large data sets.

Architecture Differences

Both cards use the Kepler architecture on TSMC’s 28 nm process, but they employ different chips. The Quadro K4200 uses the GK104 chip with 3,540 million transistors on a 294 mm² die, resulting in a transistor density of 12.0M per mm². The Tesla K20c uses the larger GK110 chip, packing 7,080 million transistors on a 561 mm² die, with a slightly higher transistor density of 12.6M per mm². The Tesla K20c’s die is nearly double the size, which explains its higher compute capacity.

Memory configurations differ substantially. The Quadro K4200 has 4 GB of GDDR5 on a 256-bit bus, running at 1350 MHz (5.4 Gbps effective), producing 172.8 GB/s of bandwidth. The Tesla K20c has 5 GB of GDDR5 on a 320-bit bus, running at 1300 MHz (5.2 Gbps effective), producing 208.0 GB/s of bandwidth. While the Tesla K20c’s memory clock is slightly lower, its wider bus gives it a 20.4% bandwidth advantage.

Compute resources are the largest differentiator. The Quadro K4200 has 1,344 shading units, 112 TMUs, and 32 ROPs. The Tesla K20c has 2,496 shading units, 208 TMUs, and 40 ROPs. This translates to a pixel rate of 36.71 GPixel/s for the Tesla K20c versus 21.95 GPixel/s for the Quadro K4200. The Tesla K20c also has a higher texture rate of 146.8 GTexel/s versus 87.81 GTexel/s.

Power and physical requirements also differ. The Quadro K4200 is a single-slot card with a 108 W TDP, requiring one 6-pin power connector and a suggested 300 W PSU. It measures 241 mm in length and 111 mm in height. The Tesla K20c is a dual-slot card with a 225 W TDP, requiring one 6-pin and one 8-pin connector, and a suggested 550 W PSU. It is longer at 267 mm. The Quadro K4200 has display outputs (1x DVI, 2x DisplayPort 1.2), while the Tesla K20c has no display outputs, confirming its compute-only orientation.

Both cards support DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175, and both use a PCIe 2.0 x16 interface. The Quadro K4200 was released on 2014-07-21, while the Tesla K20c predates it, launching on 2012-11-11.

The Verdict

The benchmark data clearly favors the Quadro K4200 in the single measured OpenCL test, with a 7.3% lead over the Tesla K20c. The Quadro K4200 also holds a higher average benchmark score (12,398 vs. 11,479) and a higher percentile ranking (52nd vs. 51st). For users who rely on OpenCL compute performance as the primary metric, the Quadro K4200 is the stronger choice.

However, the Tesla K20c’s hardware specifications tell a different story. With 2,496 shading units, 208 TMUs, and 40 ROPs, it has substantially more compute throughput capacity than the Quadro K4200. Its FP32 performance of 3.524 TFLOPS is 67.2% higher, and its memory bandwidth of 208.0 GB/s is 20.4% higher. The Tesla K20c also has 5 GB of memory versus 4 GB, which is useful for larger datasets. The Tesla K20c’s launch MSRP was 3,199 USD, which can be noted for historical context.

The practical verdict depends on the workload. For OpenCL-based applications that match the Geekbench test profile, the Quadro K4200 is the better performer. For compute workloads that stress raw shader throughput, texture fill, or memory bandwidth, the Tesla K20c’s architectural advantages are compelling. The Tesla K20c’s lack of display outputs means it must be paired with a separate display adapter, whereas the Quadro K4200 can drive displays directly.

FAQ

Q: Which card wins the head-to-head OpenCL benchmark?

A: The NVIDIA Quadro K4200 wins the Geekbench OpenCL test with a score of 12,313 against the Tesla K20c’s 11,479, a 7.3% difference.

Q: Does the Tesla K20c have more compute cores than the Quadro K4200?

A: Yes, the Tesla K20c has 2,496 shading units, while the Quadro K4200 has 1,344 shading units, nearly double the count.

Q: What is the memory bandwidth difference between the two cards?

A: The Tesla K20c offers 208.0 GB/s of bandwidth from its 320-bit bus, while the Quadro K4200 provides 172.8 GB/s from a 256-bit bus, a 20.4% advantage for the Tesla K20c.

Q: Which card has a higher FP32 performance rating?

A: The Tesla K20c is rated at 3.524 TFLOPS, compared to the Quadro K4200’s 2.107 TFLOPS, making the Tesla K20c 67.2% faster in FP32 throughput.

Q: Are there physical size differences between the two cards?

A: Yes, the Quadro K4200 is a single-slot card measuring 241 mm in length, while the Tesla K20c is a dual-slot card measuring 267 mm in length.

Q: Can both cards output video to displays?

A: The Quadro K4200 has 1x DVI and 2x DisplayPort 1.2 outputs, while the Tesla K20c has no display outputs, making it strictly a compute accelerator.

Head-to-Head Benchmarks

The only direct benchmark comparison in the data is the Geekbench OpenCL test. The NVIDIA Quadro K4200 scores 12,313 points, while the NVIDIA Tesla K20c scores 11,479 points. This gives the Quadro K4200 a 7.3% win, the only head-to-head victory recorded in the data. The Quadro K4200’s average benchmark score of 12,398 further confirms its edge, as it sits 8.0% above the Tesla K20c’s average of 11,479.

Looking at nearest rival comparisons, the Quadro K4200’s 12,313 OpenCL score places it close to the NVIDIA Tesla K20Xm (12,625, 1.8% higher) and the AMD Radeon RX 7600M XT (12,710, 2.5% higher). It also sits above the NVIDIA GeForce GTX 960A (11,998, 3.3% lower). The Tesla K20c’s 11,479 score is near the AMD Radeon Pro 5500M (11,528, 0.4% higher) and the AMD Radeon RX 7800 XT (11,627, 1.3% higher), while being 1.9% above the NVIDIA GeForce GTX 780M (11,261).

The raw specification deltas are more pronounced than the benchmark deltas. The Tesla K20c’s FP32 rating of 3.524 TFLOPS versus the Quadro K4200’s 2.107 TFLOPS represents a 67.2% gap, yet the OpenCL benchmark shows only a 7.3% difference in the opposite direction. Similarly, the Tesla K20c’s texture rate of 146.8 GTexel/s is 67.2% higher than the Quadro K4200’s 87.81 GTexel/s, and its pixel rate of 36.71 GPixel/s is 67.2% higher than 21.95 GPixel/s. These consistent 67.2% deltas across compute metrics suggest the Tesla K20c has a hardware ceiling that the OpenCL benchmark does not fully utilize.

In practical terms, the 7.3% benchmark win for the Quadro K4200 is modest, but it is the only measured performance data available. The Tesla K20c’s hardware advantages in shader count, memory capacity, and bandwidth position it for workloads that the synthetic benchmark does not capture. The data shows a card that is 67.2% faster on paper but 7.3% slower in the one test run, highlighting the importance of workload-specific evaluation.