NVIDIA Quadro RTX 5000 vs NVIDIA Tesla K20m Comparison

Head-to-Head Benchmarks

The recorded data leaves no ambiguity: the NVIDIA Quadro RTX 5000 dominates the NVIDIA Tesla K20m in every head-to-head benchmark where both were measured. The database includes two direct comparisons, Geekbench OpenCL and Geekbench Vulkan, and the Quadro RTX 5000 wins both by margins that are difficult to overstate.

In Geekbench OpenCL, the Quadro RTX 5000 scores 78,999 against the Tesla K20m's 16,241. That is a 386.4% advantage, meaning the newer card delivers nearly five times the raw compute throughput in this API. The gap is not marginal or incremental; it is a generational chasm. For any workload that relies on OpenCL compute, the Quadro RTX 5000 is in a different performance class entirely.

Geekbench Vulkan tells a similar story, though with a slightly narrower margin. The Quadro RTX 5000 posts 92,309, while the Tesla K20m manages 21,936. The delta is 320.8%. This result confirms that the Quadro's advantage is not limited to a single API or compute model; it extends across modern graphics and compute interfaces. The Vulkan score is particularly telling because Vulkan is a low-overhead API that exposes hardware capabilities more directly than older interfaces, and the Quadro RTX 5000's Turing architecture clearly benefits from that exposure.

The wins tally reflects this sweep: the Quadro RTX 5000 takes 2 wins, the Tesla K20m takes 0. There is no benchmark in the database where the older card comes out ahead. That is a clean, unambiguous result.

Context from the broader database reinforces the scale of this victory. The Quadro RTX 5000's average benchmark score is 21,629, which places it in the 67th percentile of all GPUs. The Tesla K20m's average is 19,089, good for the 64th percentile. While both cards sit in the upper half of the database, the raw scores show that the Quadro RTX 5000 is substantially faster, and the percentile difference understates the true performance gap because the Tesla K20m's two benchmark results are much lower than the Quadro's nine results.

The nearest rivals for each card also help calibrate expectations. The Quadro RTX 5000's closest competitor is the NVIDIA GeForce GTX 1060 6 GB, which has an average score of 21,856, just 1% higher. The RTX A4000 Mobile is 1.2% higher, and the AMD Radeon HD 8970M is 1.8% higher. These are tight margins, meaning the Quadro RTX 5000 sits in a competitive cluster where small differences matter. The Tesla K20m, by contrast, is nearly tied with the NVIDIA GeForce RTX 4050 Mobile (0.2% higher), the AMD Radeon RX 6600 (0.3% higher), and the NVIDIA Quadro K6000 (0.3% higher). It also trails the GeForce GTX 780 by 0.4%. The Tesla K20m is competitive with that generation of hardware, but it is simply outclassed by the Quadro RTX 5000.

Where Each One Wins

The Quadro RTX 5000 wins in every measurable category in this comparison, but the nature of those wins matters for real-world use cases. The Geekbench OpenCL result, with a 386.4% advantage, points to workloads that stress general-purpose compute on the GPU. OpenCL is commonly used in scientific simulation, image processing, and financial modeling, where large parallel workloads are the norm. The Quadro RTX 5000's 11.15 TFLOPS of FP32 performance and 22.30 TFLOPS of FP16 performance (with a 2:1 ratio) give it a massive compute advantage over the Tesla K20m's 3.524 TFLOPS of FP32 and no FP16 support at all. For any application that can use FP16, the Quadro RTX 5000 has a further edge that the Tesla K20m cannot match.

The Geekbench Vulkan result, with a 320.8% advantage, points to real-time rendering and modern graphics workloads. Vulkan is the API of choice for many game engines, CAD visualization tools, and virtual reality applications. The Quadro RTX 5000's support for Vulkan 1.4, compared to the Tesla K20m's 1.2.175, means it can take advantage of newer features and optimizations. The Quadro RTX 5000 also has dedicated ray tracing cores (48) and tensor cores (384), which the Tesla K20m lacks entirely. These hardware units accelerate workloads that the Kepler architecture cannot handle efficiently or at all.

For memory bandwidth, the Quadro RTX 5000 offers 448.0 GB/s versus the Tesla K20m's 208.0 GB/s, a 115% advantage that affects any data-intensive workload. The Quadro also has 16 GB of GDDR6 memory versus 5 GB of GDDR5, so it can hold larger datasets and models without spilling to system memory. The Tesla K20m's 320-bit bus is wider than the Quadro's 256-bit bus, but the GDDR6 memory runs at a much higher effective speed, so the bandwidth comparison still favors the Quadro decisively.

The Tesla K20m does hold one niche advantage: it is a compute-only card with no display outputs, which means it is designed for headless compute nodes where power and cooling are dedicated to computation rather than graphics output. In a server or rack environment, this can be a deliberate design choice. However, the data shows that even in pure compute benchmarks, the Tesla K20m is far slower than the Quadro RTX 5000, so this niche advantage does not translate into a performance win.

Architecture Differences

The two cards come from different architectural generations, and the gap is visible in every major component. The Quadro RTX 5000 uses the TU104 chip based on Turing architecture, manufactured on a 12 nm process at TSMC. The Tesla K20m uses the GK110 chip based on Kepler architecture, also from TSMC but on a 28 nm process. The process node difference alone explains much of the performance and efficiency gap: 12 nm allows for much higher transistor density, 25.0 million transistors per square millimeter versus 12.6 million, and the Quadro packs 13,600 million transistors onto a 545 mm² die, while the Tesla K20m has 7,080 million transistors on a slightly larger 561 mm² die.

The transistor counts tell a story of architectural evolution. The Quadro RTX 5000 has nearly twice the transistors of the Tesla K20m, despite a smaller die. This allows for 3,072 shading units, 192 texture mapping units, and 64 ROPs. The Tesla K20m has 2,496 shading units, 208 TMUs, and 40 ROPs. Interestingly, the Tesla K20m has more TMUs than the Quadro, but the Quadro compensates with much higher clock speeds: 1620 MHz base and 1815 MHz boost versus no base or boost clock listed for the Tesla K20m. The Quadro's pixel rate is 116.2 GPixel/s versus 36.71 GPixel/s for the Tesla, and its texture rate is 348.5 GTexel/s versus 146.8 GTexel/s. These are massive advantages in rasterization throughput.

The most significant architectural divergence is in specialized compute units. The Quadro RTX 5000 includes 48 ray tracing cores and 384 tensor cores, which are entirely absent from the Tesla K20m. Ray tracing cores accelerate real-time ray-traced rendering, a feature that simply does not exist in Kepler. Tensor cores accelerate deep learning inference and training, giving the Quadro RTX 5000 a capability that the Tesla K20m cannot emulate through any software workaround. The Tesla K20m has no FP16 support, while the Quadro delivers 22.30 TFLOPS of FP16 performance.

The memory subsystems also differ fundamentally. The Quadro uses 16 GB of GDDR6 on a 256-bit bus, while the Tesla K20m uses 5 GB of GDDR5 on a 320-bit bus. The GDDR6 memory runs at 1750 MHz with 14 Gbps effective speed, versus 1300 MHz with 5.2 Gbps effective for the GDDR5. The bandwidth comparison is decisive: 448.0 GB/s versus 208.0 GB/s. The Tesla K20m's wider bus cannot overcome the slower memory technology.

The API support reflects the architectural age. The Quadro RTX 5000 supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The Tesla K20m, despite being listed with DirectX 12, only supports the 11_0 feature level, so it cannot use modern DirectX 12 features like mesh shaders or variable rate shading. Both support OpenGL 4.6, but the Vulkan versions differ: 1.4 versus 1.2.175.

Specification Differences

The two cards differ in nearly every specification field, and the pattern is consistent: the Quadro RTX 5000 is newer, faster, and more capable in every measurable way.

The process node is 12 nm for the Quadro versus 28 nm for the Tesla. Transistor count is 13,600 million versus 7,080 million. Die size is 545 mm² versus 561 mm², with the Quadro achieving a higher density of 25.0M transistors per mm² versus 12.6M.

Clock speeds: the Quadro has a base clock of 1620 MHz and a boost clock of 1815 MHz. The Tesla K20m has no base or boost clock listed. Memory clock is 1750 MHz with 14 Gbps effective for the Quadro versus 1300 MHz with 5.2 Gbps effective for the Tesla.

Memory: 16 GB of GDDR6 on a 256-bit bus with 448.0 GB/s bandwidth versus 5 GB of GDDR5 on a 320-bit bus with 208.0 GB/s bandwidth.

Compute units: the Quadro has 3,072 shading units, 192 TMUs, and 64 ROPs. The Tesla has 2,496 shading units, 208 TMUs, and 40 ROPs. The Quadro has 48 RT cores and 384 tensor cores; the Tesla has none.

Performance rates: the Quadro delivers 116.2 GPixel/s and 348.5 GTexel/s, with 11.15 TFLOPS FP32 and 22.30 TFLOPS FP16. The Tesla delivers 36.71 GPixel/s and 146.8 GTexel/s, with 3.524 TFLOPS FP32 and no FP16.

Power and board: both have a TDP of 230 W versus 225 W, use dual-slot cooling, require 1x 6-pin plus 1x 8-pin power connectors, and recommend a 550 W PSU. Both are 267 mm long. The Quadro is 111 mm high, while the Tesla's height is not listed. The Quadro uses PCIe 3.0 x16, the Tesla uses PCIe 2.0 x16.

Outputs: the Quadro has 4x DisplayPort 1.4a and 1x USB Type-C. The Tesla has no outputs.

API support: DirectX 12 Ultimate (12_2) versus DirectX 12 (11_0), OpenGL 4.6 for both, Vulkan 1.4 versus 1.2.175.

Release dates: the Quadro was released in August 2018, the Tesla in January 2013. The Quadro's predecessor is Quadro Volta and successor is Workstation Ampere. The Tesla's predecessor is Tesla Fermi and successor is Tesla Maxwell. Both are end-of-life.

FAQ

Q: Which card is faster in Geekbench OpenCL?

A: The Quadro RTX 5000 scores 78,999 versus the Tesla K20m's 16,241, a 386.4% advantage for the Quadro.

Q: Does the Tesla K20m have any ray tracing capability?

A: No. The Tesla K20m has no ray tracing cores listed, while the Quadro RTX 5000 has 48 dedicated RT cores.

Q: How much memory bandwidth does each card have?

A: The Quadro RTX 5000 has 448.0 GB/s from 16 GB of GDDR6 on a 256-bit bus. The Tesla K20m has 208.0 GB/s from 5 GB of GDDR5 on a 320-bit bus.

Q: Which card supports FP16 compute?

A: Only the Quadro RTX 5000, which delivers 22.30 TFLOPS of FP16 performance. The Tesla K20m has no FP16 performance listed.

Q: Can the Tesla K20m output video to a display?

A: No, the Tesla K20m has no display outputs. The Quadro RTX 5000 has 4x DisplayPort 1.4a and 1x USB Type-C.

Q: How do their average benchmark scores compare?

A: The Quadro RTX 5000 has an average score of 21,629, placing it in the 67th percentile. The Tesla K20m has an average of 19,089, in the 64th percentile.

The Verdict

The data supports only one conclusion: the NVIDIA Quadro RTX 5000 is the superior card in every measured category. It wins both head-to-head benchmarks by margins of 386.4% and 320.8%, has more than three times the FP32 performance, quadruple the memory bandwidth, and offers ray tracing and tensor cores that the Tesla K20m simply does not have. The Tesla K20m's only advantage is its compute-only form factor with no display outputs, which may suit specific headless server deployments, but that does not compensate for its vastly lower performance.

The Quadro RTX 5000 should be the choice for anyone needing modern graphics, real-time ray tracing, deep learning acceleration, or high-bandwidth memory access. Its 16 GB of GDDR6 memory and 448.0 GB/s bandwidth make it suitable for large datasets, and its Vulkan 1.4 and DirectX 12 Ultimate support ensure compatibility with current and future software. The Tesla K20m, with its 5 GB of GDDR5 and 208.0 GB/s bandwidth, plus no FP16, no ray tracing, and no tensor cores, is a legacy compute card that the database shows is far out of its league.

For buyers choosing between these two, the Quadro RTX 5000 is the only rational pick based on performance data. The Tesla K20m remains functional for basic compute tasks, but the recorded benchmarks demonstrate that it is not competitive with the Quadro RTX 5000 in any workload measured.