GPU Comparison

The NVIDIA Quadro K5200 and NVIDIA Tesla K20m are both 28 nm Kepler-generation parts built on the same GK110-class silicon, yet benchmark results show they are not interchangeable. Across the two Geekbench tests, each card takes one win, with the Quadro K5200 dominating OpenCL while the Tesla K20m counters in Vulkan. The average benchmark scores place them nearly level, 19,602 for the Quadro K5200 versus 19,089 for the Tesla K20m, a gap of roughly 2.7%, which is well within the noise of typical driver and workload variance. Both cards sit at the 64th percentile among all GPUs, indicating that despite their age and end-of-life status, they remain mid-pack performers in the current database.

Head-to-Head Benchmarks

The most decisive result in this matchup is the Geekbench OpenCL test, where the Quadro K5200 scores 19,024 against the Tesla K20m’s 16,241. That is a 17.1% advantage for the Quadro, a substantial margin that suggests the K5200’s driver stack and memory configuration are better suited to general-purpose compute workloads measured by OpenCL. The K5200’s 8 GB frame buffer and 192.3 GB/s of bandwidth appear to provide a tangible benefit here, even though the Tesla has a wider 320-bit memory bus and higher raw bandwidth of 208.0 GB/s. The OpenCL result anchors the Quadro’s case as the better all-around compute card for applications that rely on this API.

However, the Vulkan benchmark flips the script. The Tesla K20m posts 21,936, which is 8% higher than the Quadro K5200’s 20,180. This is a noteworthy reversal, as the Tesla’s higher shading unit count, 2,496 versus 2,304, and its 208 texture units versus 192 may give it an edge in workloads that scale with raw shader throughput, even at lower clock speeds. The deltaPct of -8% in favor of the Tesla shows that the K20m is not merely competitive in Vulkan; it is clearly ahead. This split result, one win each with a 17.1% margin for the Quadro and an 8% margin for the Tesla, paints a picture of two cards that trade blows depending on the API and optimization level.

Looking at the nearest rivals for context, the Quadro K5200’s average score of 19,602 places it within 1% of the AMD FirePro D300 (19,637, deltaPct -0.2%) and the AMD Radeon RX 6650 XT (19,765, deltaPct -0.8%). It also edges out the AMD Radeon RX 7900 XTX by 1% (deltaPct 1) and the NVIDIA GeForce GTX 1060 3 GB by 1.4% (deltaPct 1.4). The Tesla K20m’s average of 19,089 is similarly clustered, sitting just 0.2% above the NVIDIA GeForce RTX 4050 Mobile (19,049) and 0.3% above both the AMD Radeon RX 6600 (19,036) and the NVIDIA Quadro K6000 (19,030). It trails the NVIDIA GeForce GTX 780 (19,164) by 0.4%. These rival deltas confirm that both cards are essentially equivalent in aggregate performance, with the individual benchmark wins being the only meaningful differentiators.

FAQ

Q: Which card wins the OpenCL benchmark, and by how much?

A: The NVIDIA Quadro K5200 wins Geekbench OpenCL with a score of 19,024 compared to the Tesla K20m’s 16,241, a 17.1% advantage.

Q: Does the Tesla K20m win any benchmark against the Quadro K5200?

A: Yes, the Tesla K20m wins Geekbench Vulkan with a score of 21,936 versus the Quadro K5200’s 20,180, an 8% margin.

Q: How do the average benchmark scores compare between the two cards?

A: The Quadro K5200 has an average benchmark score of 19,602, while the Tesla K20m averages 19,089. The Quadro leads by roughly 2.7%.

Q: What are the percentile rankings for these GPUs?

A: Both the Quadro K5200 and the Tesla K20m sit at the 64th percentile among all GPUs in the database.

Q: Which card has more shading units and texture units?

A: The Tesla K20m has 2,496 shading units and 208 texture units, while the Quadro K5200 has 2,304 shading units and 192 texture units.

Q: What is the memory size and bandwidth difference?

A: The Quadro K5200 has 8 GB of GDDR5 memory with 192.3 GB/s bandwidth, while the Tesla K20m has 5 GB of GDDR5 memory with 208.0 GB/s bandwidth.

Where Each One Wins

The Quadro K5200 wins in OpenCL compute workloads, as evidenced by its 17.1% lead in that specific benchmark. This makes it the stronger choice for applications that rely heavily on OpenCL for general-purpose GPU computing, such as certain scientific simulation, image processing, or financial modeling tasks. Its larger 8 GB memory capacity also gives it an advantage in scenarios where working sets exceed the Tesla’s 5 GB allocation, even though the Tesla has higher peak bandwidth. The Quadro’s display outputs (2x DVI and 2x DisplayPort 1.2) mean it can drive a monitor directly, making it viable for workstation use where visual output is required.

The Tesla K20m wins in Vulkan, posting an 8% higher score. Vulkan is a low-overhead API that often benefits from raw compute unit counts, and the Tesla’s 2,496 shading units and 208 texture units provide more parallel execution resources than the Quadro’s 2,304 and 192, respectively. This makes the Tesla the better pick for Vulkan-based rendering or compute pipelines, which are increasingly common in modern game engines and professional visualization tools. The Tesla’s higher memory bandwidth of 208.0 GB/s also helps in bandwidth-sensitive Vulkan workloads, even if its smaller 5 GB capacity limits total data residency.

For users whose primary metric is average score across both tests, the Quadro K5200 holds a slight edge at 19,602 versus 19,089. But the Tesla’s win in Vulkan cannot be ignored, particularly for those targeting that API exclusively. The split is even at one win apiece, so the decision hinges on which API the user’s software stack prioritizes.

Specification Differences

The two cards diverge on nearly every key specification except the underlying chip, process node, and transistor count. The Quadro K5200 uses the GK110B chip, while the Tesla K20m uses the GK110. Both are built on TSMC’s 28 nm process with 7,080 million transistors on a 561 mm² die, yielding a transistor density of 12.6M per mm². However, the Tesla K20m has more shading units (2,496 vs. 2,304), more texture units (208 vs. 192), but fewer ROPs (40 vs. 48). Clock speeds are not directly comparable, as the Quadro lists base and boost clocks of 667 MHz and 771 MHz, respectively, while the Tesla’s clocks are not provided in the data.

Memory configurations differ significantly. The Quadro K5200 has 8 GB of GDDR5 on a 256-bit bus, running at 1502 MHz with 6 Gbps effective speed, yielding 192.3 GB/s bandwidth. The Tesla K20m has 5 GB of GDDR5 on a 320-bit bus at 1300 MHz with 5.2 Gbps effective speed, yielding 208.0 GB/s bandwidth. The Tesla’s wider bus gives it higher bandwidth despite lower clock speeds. Pixel and texture rates are nearly identical: the Quadro posts 37.01 GPixel/s and 148.0 GTexel/s, while the Tesla posts 36.71 GPixel/s and 146.8 GTexel/s. FP32 performance is also close, at 3.553 TFLOPS for the Quadro and 3.524 TFLOPS for the Tesla.

Power and interface specs show the Tesla as the hungrier card. The Quadro K5200 has a 150 W TDP with a single 6-pin power connector and a suggested PSU of 450 W, while the Tesla K20m has a 225 W TDP with 6-pin and 8-pin connectors and a 550 W suggested PSU. The Quadro uses PCIe 3.0 x16, whereas the Tesla uses PCIe 2.0 x16. Display outputs also differ: the Quadro offers 2x DVI and 2x DisplayPort 1.2, while the Tesla has no outputs. Both are dual-slot cards and share the same 267 mm length, though only the Quadro lists a height of 111 mm.

Architecture Differences

Both cards are Kepler architecture parts from NVIDIA, but they belong to different generations and market segments. The Quadro K5200 is from the Quadro Kepler (Kx200) generation, released on 2014-07-21, while the Tesla K20m is from the Tesla Kepler (Kxx) generation, released earlier on 2013-01-04. The chip difference, GK110B for the Quadro versus GK110 for the Tesla, likely accounts for the slight variations in core counts and clock behavior. Both support DirectX 12, but the Quadro supports version 12 (11_1) while the Tesla supports 12 (11_0), a minor feature level difference. Both support OpenGL 4.6 and Vulkan 1.2.175.

The Quadro K5200’s predecessor is Quadro Fermi and its successor is Quadro Maxwell, while the Tesla K20m’s predecessor is Tesla Fermi and successor is Tesla Maxwell. The Quadro’s display outputs are a key architectural difference, as they enable traditional workstation use, whereas the Tesla is a compute-only accelerator with no display capabilities. The Tesla’s higher TDP of 225 W versus 150 W reflects its denser compute configuration, and its dual power connectors (6-pin and 8-pin) versus the Quadro’s single 6-pin indicate a different power delivery design. The Tesla also predates the Quadro by roughly 18 months, which may explain its older PCIe 2.0 interface versus the Quadro’s PCIe 3.0.

Neither card has RT cores or tensor cores, as these are pre-Ray Tracing and pre-Tensor Core architectures. FP16 performance is not listed for either, indicating a lack of hardware support for half-precision compute. Both cards are end-of-life, but their architecture differences, particularly in core counts, memory bus width, and power delivery, create the performance split observed in the benchmarks.

The Verdict

The data points to a clear recommendation based on workload. If your software stack relies on OpenCL, the NVIDIA Quadro K5200 is the unequivocal choice, delivering a 17.1% performance advantage over the Tesla K20m in that benchmark. Its 8 GB memory capacity also provides more headroom for large datasets, and its display outputs make it a functional workstation card. The Quadro’s lower TDP of 150 W and single 6-pin power connector also make it easier to integrate into existing systems without major PSU upgrades.

If Vulkan is your primary API, the NVIDIA Tesla K20m is the better performer, with an 8% lead in that test. Its higher shading unit count (2,496) and texture unit count (208) give it more raw compute throughput for Vulkan’s parallel execution model, and its higher memory bandwidth of 208.0 GB/s helps feed those units. The Tesla’s lack of display outputs is irrelevant for compute-only deployments, and its higher TDP of 225 W is acceptable in a server or dedicated compute chassis.

For users who need a balanced card for mixed workloads, the Quadro K5200’s higher average score (19,602 vs. 19,089) gives it a slight edge, but the margin is small. Both cards are end-of-life and sit at the same 64th percentile, so neither offers a meaningful performance tier advantage. The decision ultimately comes down to API priority, memory capacity needs, and power budget. The Quadro K5200 wins on OpenCL, memory size, and power efficiency; the Tesla K20m wins on Vulkan, raw core counts, and memory bandwidth. Choose accordingly.