NVIDIA Quadro RTX 5000 vs NVIDIA Tesla K40m Comparison

The NVIDIA Quadro RTX 5000 and the NVIDIA Tesla K40m are both end-of-life workstation cards, but they represent two vastly different eras of GPU design. The data shows a decisive victory for the Quadro RTX 5000, which outperforms the Tesla K40m by 297.3% in the single available head-to-head benchmark, Geekbench OpenCL. While the Tesla K40m was a high-performance compute card in its generation, the architectural and memory advantages of the Quadro RTX 5000 make it the superior choice for nearly all modern workloads.

FAQ

Q: How significant is the performance gap between the Quadro RTX 5000 and the Tesla K40m?

A: The gap is massive. In the Geekbench OpenCL benchmark, the Quadro RTX 5000 scores 78,999, which is 297.3% higher than the Tesla K40m’s score of 19,885. This indicates the Quadro RTX 5000 delivers nearly four times the compute performance in this test.

Q: Which card has a better memory subsystem?

A: The Quadro RTX 5000 is superior. It features 16 GB of GDDR6 memory on a 256-bit bus, yielding 448.0 GB/s of bandwidth. In contrast, the Tesla K40m has 12 GB of GDDR5 on a wider 384-bit bus, but its bandwidth is only 288.4 GB/s. The Quadro RTX 5000 also uses faster memory at 14 Gbps effective versus 6 Gbps effective.

Q: Are there any benchmark results where the Tesla K40m wins?

A: No. In the head-to-head data, the Tesla K40m has zero wins. The only comparable test, Geekbench OpenCL, is won by the Quadro RTX 5000. The Tesla K40m’s average benchmark score of 19,885 is also significantly lower than the Quadro RTX 5000’s average of 21,629.

Q: What are the architectural differences between the two cards?

A: The Quadro RTX 5000 uses the Turing architecture on a 12 nm process, while the Tesla K40m uses the older Kepler architecture on a 28 nm process. The Quadro RTX 5000 includes 48 RT cores and 384 Tensor cores, which are entirely absent from the Tesla K40m. The Quadro RTX 5000 also supports FP16 compute at 22.30 TFLOPS, while the Tesla K40m has no FP16 capability listed.

Q: How do the cards compare in terms of raw compute power?

A: The Quadro RTX 5000 delivers 11.15 TFLOPS of FP32 performance and 22.30 TFLOPS of FP16 performance. The Tesla K40m is limited to 5.046 TFLOPS of FP32. This means the Quadro RTX 5000 has more than double the single-precision floating-point throughput.

Q: Which card has a better standing relative to other GPUs?

A: The Quadro RTX 5000 sits in the 67th percentile of all GPUs, while the Tesla K40m is in the 65th percentile. The Quadro RTX 5000’s nearest rival is the NVIDIA GeForce GTX 1060 6 GB, which is 1% slower, whereas the Tesla K40m’s closest competitor is the AMD FirePro W7000, which is 0.1% faster.

The Verdict

The verdict is clear: the NVIDIA Quadro RTX 5000 is the definitive choice for any task that requires modern features and high compute throughput. Its 297.3% lead in Geekbench OpenCL over the Tesla K40m is not a marginal improvement; it is a generational leap. The Quadro RTX 5000 is for users who need real-time ray tracing (48 RT cores), AI acceleration (384 Tensor cores), and high-bandwidth memory (448.0 GB/s). The Tesla K40m, with its 12 GB of GDDR5 and 5.046 TFLOPS, is strictly a legacy compute card. Its 65th percentile ranking shows it is still competitive with older hardware, but it lacks the features and raw speed of the newer card. Pick the Quadro RTX 5000 for any contemporary workload; pick the Tesla K40m only if you are maintaining a legacy system that specifically requires its Kepler architecture.

Head-to-Head Benchmarks

The sole benchmark comparing the two GPUs is Geekbench OpenCL, and the results are lopsided. The Quadro RTX 5000 scores 78,999 points, while the Tesla K40m scores 19,885 points. This translates to a delta of 297.3%, meaning the Quadro RTX 5000 is nearly four times faster in this compute-oriented test. This is the biggest win across any metric in the data. The Quadro RTX 5000’s average benchmark score of 21,629 across all tests is also higher than the Tesla K40m’s average of 19,885. While the Tesla K40m has a higher percentile score in DirectX 10 (113 vs. the Quadro RTX 5000’s 113), this is a tie, not a win. In all other metrics, the Quadro RTX 5000 either leads or the data is unavailable. The Tesla K40m simply cannot compete on raw compute performance, as the OpenCL result confirms.

Specification Differences

The specifications reveal a stark contrast between the two cards. The Quadro RTX 5000 operates at a base clock of 1620 MHz and a boost clock of 1815 MHz, compared to the Tesla K40m’s much lower 745 MHz base and 876 MHz boost. Memory capacity differs: 16 GB GDDR6 for the Quadro RTX 5000 versus 12 GB GDDR5 for the Tesla K40m. The memory bus is wider on the Tesla K40m (384 bit vs. 256 bit), but the Quadro RTX 5000’s faster memory type and clock yield higher bandwidth (448.0 GB/s vs. 288.4 GB/s). The shading unit counts are close (3072 vs. 2880), but the Tesla K40m has more TMUs (240 vs. 192) and fewer ROPs (48 vs. 64). The Quadro RTX 5000 has a higher pixel rate (116.2 GPixel/s vs. 52.56 GPixel/s) and texture rate (348.5 GTexel/s vs. 210.2 GTexel/s). The Quadro RTX 5000 has a lower TDP of 230 W versus 245 W, but both suggest a 550 W power supply. The Quadro RTX 5000 includes 4x DisplayPort 1.4a and 1x USB Type-C outputs, while the Tesla K40m has no display outputs. The launch MSRP of the Quadro RTX 5000 was 2,299 USD, while the Tesla K40m launched at 7,699 USD.

Architecture Differences

The architectural gap between these two cards is generational. The Quadro RTX 5000 is built on the Turing architecture using a 12 nm process at TSMC, packing 13,600 million transistors into a 545 mm² die. The Tesla K40m uses the older Kepler architecture on a 28 nm process, with 7,080 million transistors on a slightly larger 561 mm² die. This means the Quadro RTX 5000 has a transistor density of 25.0M / mm², double the Tesla K40m’s 12.6M / mm². Crucially, the Quadro RTX 5000 introduces dedicated hardware for specialized tasks: 48 RT cores for ray tracing and 384 Tensor cores for AI workloads. The Tesla K40m has neither. The Quadro RTX 5000 also supports FP16 compute at 22.30 TFLOPS (2:1 ratio), a feature entirely absent from the Tesla K40m, which only lists FP32 at 5.046 TFLOPS. The API support differs as well: the Quadro RTX 5000 supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the Tesla K40m is limited to DirectX 12 (11_1) and Vulkan 1.2.175. The Tesla K40m is a pure compute accelerator with no display outputs, whereas the Quadro RTX 5000 is a full workstation GPU with multiple display outputs.

Where Each One Wins

The Quadro RTX 5000 wins in every measurable category. It is the clear choice for modern compute tasks, including real-time ray tracing, AI inference, and deep learning, thanks to its RT cores and Tensor cores. Its higher FP32 throughput (11.15 TFLOPS) and FP16 support (22.30 TFLOPS) make it ideal for scientific simulations and rendering. The larger memory capacity (16 GB) and higher bandwidth (448.0 GB/s) also benefit large datasets and high-resolution textures. The Tesla K40m’s only advantage is its legacy compatibility. It is an end-of-life product from 2013 that may be required for specific older software stacks that rely on Kepler compute. Its wider 384-bit memory bus is a theoretical advantage, but the slower GDDR5 memory negates this in practice. The data shows the Tesla K40m’s single benchmark score of 19,885 is lower than the Quadro RTX 5000’s average score of 21,629, meaning even the Tesla K40m’s best case falls short of the Quadro RTX 5000’s typical performance. For any new deployment, the Quadro RTX 5000 is the only rational option.