NVIDIA GeForce RTX 3050 Ti Mobile vs NVIDIA Tesla K20Xm Comparison

The data presents a clear generational clash: the NVIDIA GeForce RTX 3050 Ti Mobile, a modern Ampere-based laptop chip, against the NVIDIA Tesla K20Xm, a Kepler-based compute accelerator from 2012. The benchmark results, while limited in overlap, show a decisive victory for the newer part in compute workloads, yet the K20Xm retains distinct hardware advantages in memory capacity and raw texture throughput. The average benchmark scores are surprisingly close—12940 for the RTX 3050 Ti Mobile versus 12625 for the Tesla K20Xm—placing both within 1% of each other in overall standing, but this masks a fundamental divergence in architectural philosophy and application fit.

The Verdict

Based strictly on the available benchmark data, the NVIDIA GeForce RTX 3050 Ti Mobile is the definitive winner for general-purpose and compute-heavy tasks. Its Geekbench OpenCL score of 57915 is a staggering 236.4% higher than the Tesla K20Xm’s 17215, a margin that dwarfs any other comparison in the data. For users running OpenCL workloads, the choice is unambiguous: the RTX 3050 Ti Mobile delivers more than triple the performance. This is reinforced by its 53rd percentile ranking among all GPUs, slightly ahead of the K20Xm’s 52nd percentile.

However, the Tesla K20Xm is not without purpose. Its 6 GB of GDDR5 memory on a 384-bit bus provides 249.6 GB/s of bandwidth, which is 30% more than the RTX 3050 Ti Mobile’s 192.0 GB/s. For datasets that exceed the 4 GB frame buffer of the RTX 3050 Ti Mobile, the K20Xm becomes the only viable option, regardless of raw compute speed. The K20Xm also boasts a higher pixel rate (40.99 GPixel/s vs 33.12 GPixel/s) and a dramatically higher texture rate (164.0 GTexel/s vs 82.80 GTexel/s), suggesting it may handle certain fill-rate-bound or texture-heavy workloads more efficiently. The choice hinges on workload memory footprint and whether the massive OpenCL performance gap can be traded for additional capacity.

Architecture Differences

The two GPUs represent opposite ends of the design spectrum. The RTX 3050 Ti Mobile is built on Samsung’s 8 nm process, packing 12,000 million transistors into a 276 mm² die, yielding a transistor density of 43.5M per mm². In contrast, the Tesla K20Xm uses TSMC’s 28 nm node, with 7,080 million transistors on a much larger 561 mm² die, giving a density of just 12.6M per mm². This process advantage is a primary driver of the RTX 3050 Ti Mobile’s superior performance-per-watt, as evidenced by its 75 W TDP versus the K20Xm’s 235 W TDP.

Architecturally, the RTX 3050 Ti Mobile is built on the Ampere architecture with the GA106 chip, featuring 2560 shading units, 80 TMUs, and 32 ROPs. It also includes 20 RT cores and 80 tensor cores, enabling hardware-accelerated ray tracing and AI features absent from the older part. The FP32 compute is rated at 5.299 TFLOPS, with FP16 running at the same rate (1:1). The Tesla K20Xm, using the Kepler architecture with the GK110 chip, has slightly more shading units (2688) and significantly more TMUs (224) and ROPs (48), but its FP32 peak is lower at 3.935 TFLOPS. It has no RT or tensor cores, and its FP16 capability is not specified. The K20Xm also lacks display outputs entirely, making it a pure compute accelerator, whereas the RTX 3050 Ti Mobile is designed for portable devices with dependent display outputs.

Memory subsystems differ markedly. The RTX 3050 Ti Mobile uses 4 GB of GDDR6 on a 128-bit bus, achieving 192.0 GB/s. The K20Xm has 6 GB of GDDR5 on a 384-bit bus, reaching 249.6 GB/s. The K20Xm’s higher memory bandwidth and capacity are its primary hardware advantages. The RTX 3050 Ti Mobile supports PCIe 4.0 x16, while the K20Xm uses PCIe 3.0 x16. API support also diverges: the RTX 3050 Ti Mobile supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, while the K20Xm is limited to DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175.

Where Each One Wins

The RTX 3050 Ti Mobile wins decisively in compute performance. The single head-to-head benchmark, Geekbench OpenCL, shows it at 57915 versus 17215, a 236.4% advantage. This suggests that any OpenCL-based application—scientific simulation, image processing, or machine learning inference—will run dramatically faster on the RTX 3050 Ti Mobile. Its modern architecture also provides hardware ray tracing and tensor cores, which are absent on the K20Xm, making it the superior choice for gaming and AI-accelerated workloads in portable devices.

The Tesla K20Xm wins where memory capacity and bandwidth are critical. With 6 GB versus 4 GB, it can hold larger datasets in VRAM, avoiding costly transfers over PCIe. Its 249.6 GB/s bandwidth is 30% higher than the RTX 3050 Ti Mobile’s 192.0 GB/s, which benefits memory-bound tasks such as large matrix operations or high-resolution texture streaming. The K20Xm’s higher texture rate (164.0 GTexel/s) and pixel rate (40.99 GPixel/s) indicate strengths in rasterization-heavy workloads, although its lack of display outputs means it cannot drive a monitor directly. For server environments where a separate display adapter exists, the K20Xm’s raw fill rates could be leveraged.

FAQ

Q: Which GPU has a higher average benchmark score?

A: The RTX 3050 Ti Mobile has an average benchmark score of 12940, while the Tesla K20Xm averages 12625. The difference is 315 points, approximately 2.5%, favoring the RTX 3050 Ti Mobile.

Q: How much faster is the RTX 3050 Ti Mobile in OpenCL compute?

A: In the Geekbench OpenCL test, the RTX 3050 Ti Mobile scores 57915 versus the Tesla K20Xm’s 17215, giving the newer GPU a 236.4% performance lead.

Q: Does the Tesla K20Xm have any memory advantage?

A: Yes, the Tesla K20Xm has 6 GB of GDDR5 memory on a 384-bit bus, providing 249.6 GB/s bandwidth. The RTX 3050 Ti Mobile has 4 GB of GDDR6 on a 128-bit bus, yielding 192.0 GB/s.

Q: What is the power consumption difference?

A: The RTX 3050 Ti Mobile has a TDP of 75 W, while the Tesla K20Xm draws 235 W. The K20Xm also requires a 550 W suggested PSU, whereas the RTX 3050 Ti Mobile uses no power connectors.

Q: Which GPU supports ray tracing?

A: Only the RTX 3050 Ti Mobile supports ray tracing, as it includes 20 RT cores. The Tesla K20Xm has no RT cores or tensor cores.

Q: Are there any benchmark tests where the Tesla K20Xm wins?

A: In the provided head-to-head data, the Tesla K20Xm wins zero tests. The RTX 3050 Ti Mobile wins the only overlapping benchmark (Geekbench OpenCL).

Head-to-Head Benchmarks

The only directly comparable benchmark between the two GPUs is Geekbench OpenCL, and the result is lopsided. The RTX 3050 Ti Mobile scores 57915, while the Tesla K20Xm manages 17215. This represents a 236.4% delta, meaning the RTX 3050 Ti Mobile delivers over three times the OpenCL compute performance. This is the single largest performance gap in the entire dataset, and it is not close to any other rival comparison. For context, the RTX 3050 Ti Mobile’s nearest rival, the AMD Radeon RX 580, has an average score of 12928, which is only 0.1% different from the RTX 3050 Ti Mobile’s own average. The Tesla K20Xm’s nearest rival, the AMD Radeon RX 7600M XT, has an average score of 12710, which is 0.7% higher than the K20Xm’s average. This indicates that while the two GPUs are statistically similar in aggregate benchmarks, the OpenCL test reveals a massive architectural divide.

Beyond the head-to-head, the RTX 3050 Ti Mobile’s other benchmark scores reinforce its compute strengths. Its PassMark G3D score is 10093, and its Geekbench Vulkan score is 55812, showing strong performance across multiple APIs. The Tesla K20Xm only has two benchmarks listed: Geekbench Metal at 8035, and Geekbench OpenCL at 17215. The absence of DirectX and Vulkan scores for the K20Xm suggests that its legacy architecture is not competitive in modern graphics APIs. The RTX 3050 Ti Mobile’s DirectX 12 Ultimate support and Vulkan 1.4 compatibility give it a software ecosystem advantage that the K20Xm, limited to DirectX 12 (11_0) and Vulkan 1.2.175, cannot match. In every measurable compute dimension, the RTX 3050 Ti Mobile is the superior part; the K20Xm’s only counterarguments are its larger memory pool and higher fill rates, which do not show up as wins in the available benchmark suite.