GPU Comparison

The NVIDIA Tesla K40m and NVIDIA Tesla K20m are both professional-grade Kepler architecture accelerators aimed at compute workloads, but the benchmark data shows a clear performance hierarchy between them. In the single available head-to-head benchmark, the Geekbench OpenCL test, the K40m delivers a score of 19,885 against the K20m’s 16,241, giving the K40m a decisive 22.4% advantage. This is a substantial gap in raw compute throughput, reflecting the K40m’s position as the higher-tier part in this comparison. The data indicates that while both cards belong to the same family and share a common design language, the K40m consistently outpaces the K20m in the measured workload, making it the stronger choice for performance-sensitive applications.

Head-to-Head Benchmarks

The only direct benchmark comparison available is Geekbench OpenCL, and the result is unambiguous. The NVIDIA Tesla K40m scores 19,885 points, while the NVIDIA Tesla K20m trails with 16,241 points. This translates to a 22.4% lead for the K40m, a significant margin that underscores the architectural and configuration advantages the newer card brings to the table. In practical terms, this means the K40m completes OpenCL compute tasks notably faster, which is critical for users running simulations, rendering, or scientific calculations that leverage GPU acceleration.

It is worth noting where these scores place each card relative to their broader market peers. The K40m’s score of 19,885 puts it at the 65th percentile of all GPUs, and its nearest rival is the AMD FirePro W7000, which scores 19,905 and is effectively tied with it (deltaPct of -0.1%). This suggests that the K40m is competitive with contemporary workstation cards in its class. The K20m, with its average benchmark score of 19,089 (derived from its OpenCL and Vulkan results), sits at the 64th percentile, with its nearest rival being the NVIDIA GeForce RTX 4050 Mobile at 19,049 (0.2% delta). The K20m’s average score is pulled up by its Vulkan result of 21,936, but in the OpenCL test that matters for this head-to-head, it falls well short of the K40m.

The deltaPct values in the K40m’s rival list further contextualize its performance. It beats the AMD Radeon RX 6650 XT by 0.6%, the AMD FirePro D300 by 1.3%, and the NVIDIA Quadro K5200 by 1.4%. These are tight margins, indicating that the K40m is a well-balanced performer in its segment. The K20m, conversely, shows a negative deltaPct of -0.4% against the NVIDIA GeForce GTX 780, meaning it is slightly slower than that gaming card in the aggregate, though it edges out the RTX 4050 Mobile, RX 6600, and Quadro K6000 by 0.2% to 0.3%. The head-to-head data, however, is the clearest signal: the K40m is the faster card by a wide margin in OpenCL.

Architecture Differences

Both cards are built on the same fundamental architecture, but NVIDIA has made key changes that explain the performance gap. The K40m uses the GK110B chip, while the K20m uses the original GK110. Physically, they are nearly identical: both are fabricated on a 28 nm process at TSMC, pack 7,080 million transistors, and have a die size of 561 mm², resulting in the same transistor density of 12.6M / mm². The core counts, however, differ. The K40m carries 2,880 shading units, 240 texture mapping units (TMUs), and 48 render output units (ROPs). The K20m has fewer of each: 2,496 shading units, 208 TMUs, and 40 ROPs. This represents a 15.4% increase in shading units and a 15.4% increase in TMUs for the K40m over the K20m, which directly translates to higher theoretical throughput.

Clock speeds further widen the gap. The K40m runs at a base clock of 745 MHz with a boost clock of 876 MHz, whereas the K20m’s clock specifications are not listed, but its memory clock is lower at 1,300 MHz (5.2 Gbps effective) compared to the K40m’s 1,502 MHz (6 Gbps effective). This higher memory clock contributes to the K40m’s superior memory bandwidth of 288.4 GB/s versus the K20m’s 208.0 GB/s, a difference of over 38%. Memory configuration also differs: the K40m comes with 12 GB of GDDR5 on a 384-bit bus, while the K20m has 5 GB on a narrower 320-bit bus. These factors combine to give the K40m a peak FP32 performance of 5.046 TFLOPS, significantly higher than the K20m’s 3.524 TFLOPS, which is a 43% advantage in raw floating-point compute.

Connectivity and power also differentiate the two. The K40m uses a PCIe 3.0 x16 interface, while the K20m is limited to PCIe 2.0 x16, which could affect data transfer speeds in bandwidth-sensitive workloads. The K40m has a higher TDP of 245 W compared to the K20m’s 225 W, and it requires no specific power connector listing, whereas the K20m needs a 1x 6-pin + 1x 8-pin configuration. Both are dual-slot cards with the same length of 267 mm (10.5 inches) and have no display outputs, confirming their compute-only purpose. In terms of API support, the K40m supports DirectX 12 (11_1), while the K20m supports DirectX 12 (11_0), a minor difference. Both support OpenGL 4.6 and Vulkan 1.2.175.

Where Each One Wins

Based on the data, the NVIDIA Tesla K40m wins the compute performance category outright. Its higher core count, faster clocks, and larger memory subsystem make it the superior choice for any workload that stresses FP32 throughput, such as scientific simulations, deep learning inference (in its era), or complex 3D rendering. The 22.4% lead in Geekbench OpenCL is a direct indicator that the K40m will complete these tasks faster, and the 43% advantage in peak FP32 TFLOPS reinforces this. For users who need maximum compute density in a single slot, the K40m is the clear pick.

The K20m, while slower, does have a niche based on its specifications. It has a lower TDP of 225 W versus the K40m’s 245 W, which might make it slightly more manageable in power-constrained environments, though both require a 550 W suggested PSU. The K20m also shows a strong Vulkan score of 21,936, which is higher than its OpenCL score, suggesting that it might handle Vulkan-based compute workloads relatively better, though this is not directly comparable to the K40m since the K40m lacks a Vulkan benchmark result in the data. In terms of percentile ranking, the K20m sits at 64th percentile versus the K40m’s 65th, which is nearly identical, indicating that both are similarly positioned in the broader GPU landscape.

For memory capacity, the K40m’s 12 GB is more than double the K20m’s 5 GB, making it far better suited for datasets that exceed 5 GB, such as large-scale matrix operations or high-resolution texture sets. The K20m, with its smaller memory, would hit capacity limits sooner, forcing data swapping that degrades performance. However, if a workload specifically requires less memory and benefits from the K20m’s lower power draw, it could be a viable alternative, but the K40m’s performance advantages are so pronounced that it is difficult to recommend the K20m on any performance basis.

The Verdict

The data is unequivocal: the NVIDIA Tesla K40m outperforms the NVIDIA Tesla K20m in every measurable compute metric. The 22.4% lead in the only head-to-head benchmark, combined with higher core counts, faster memory, and a larger memory pool, makes the K40m the superior accelerator for demanding compute workloads. Users who prioritize raw performance, memory capacity, or bandwidth should choose the K40m without hesitation, as it delivers a 43% higher FP32 throughput and 38% more memory bandwidth than the K20m.

The K20m, however, is not without merit. For systems where power consumption is a critical constraint, the K20m’s 225 W TDP (compared to 245 W) offers a modest reduction, and its narrower PCIe 2.0 interface might be acceptable in older platforms. Its Vulkan score of 21,936 suggests it can handle Vulkan compute tasks competently, but since the K40m lacks a Vulkan benchmark, a direct comparison in that API is impossible from the data. If a user has a workload that fits within 5 GB of memory and is power-sensitive, the K20m could serve adequately, but it would be a sacrifice in performance.

From a competitive standpoint, the K40m’s nearest rival is the AMD FirePro W7000, which is effectively tied with it (0.1% delta), while the K20m is closest to the NVIDIA GeForce RTX 4050 Mobile (0.2% delta). This suggests that the K40m is a more polished, higher-end product, whereas the K20m is closer to mid-range mobile and desktop parts. The K40m’s 12 GB memory is also a forward-looking feature for larger datasets. In summary, select the K40m for maximum performance and future-proofing; select the K20m only if its lower power draw or specific Vulkan capabilities are paramount.

FAQ

Q: How much faster is the NVIDIA Tesla K40m than the K20m in the head-to-head benchmark?

A: The K40m scores 19,885 in Geekbench OpenCL, while the K20m scores 16,241, giving the K40m a 22.4% lead.

Q: What are the key differences in core configuration?

A: The K40m has 2,880 shading units, 240 TMUs, and 48 ROPs, whereas the K20m has 2,496 shading units, 208 TMUs, and 40 ROPs, representing a 15.4% increase in shading units for the K40m.

Q: Do the cards differ in memory size and bandwidth?

A: Yes, the K40m has 12 GB of GDDR5 with 288.4 GB/s bandwidth on a 384-bit bus, while the K20m has 5 GB with 208.0 GB/s on a 320-bit bus.

Q: Which card has a higher peak FP32 performance?

A: The K40m delivers 5.046 TFLOPS, which is 43% higher than the K20m’s 3.524 TFLOPS.

Q: Are there any differences in power consumption?

A: The K40m has a TDP of 245 W, while the K20m has a lower TDP of 225 W, though both have a suggested PSU of 550 W.

Q: What interface does each card use?

A: The K40m uses PCIe 3.0 x16, while the K20m uses the older PCIe 2.0 x16 interface.