AMD Radeon Pro Vega 16 vs NVIDIA Tesla K20m Comparison

AMD Radeon Pro Vega 16 and NVIDIA Tesla K20m are two end-of-life accelerators from different eras, one a mobile-class integrated GPU from AMD’s Vega generation and the other a dual-slot compute board from NVIDIA’s Kepler line. The recorded data shows a split decision: the AMD part wins one benchmark, the NVIDIA part wins the other, and the margin of victory is not uniform. The AMD Radeon Pro Vega 16 lands at the 68th percentile among all GPUs, while the NVIDIA Tesla K20m sits at the 64th percentile, a modest but meaningful gap in the database’s overall ranking.

Where Each One Wins

The AMD Radeon Pro Vega 16 takes the OpenCL workload decisively. Its Geekbench OpenCL score of 18,268 beats the Tesla K20m’s 16,241 by 12.5%, a clear margin that suggests the AMD architecture handles this particular compute API more efficiently. This win is notable because the Tesla K20m has far more shading units and a higher raw FP32 throughput, yet the Vega 12 chip still comes out ahead in the measured OpenCL test. The implication is that the AMD part’s memory subsystem, with 307.2 GB/s of bandwidth over a 1024-bit HBM2 interface, compensates for its lower shader count in this workload.

The NVIDIA Tesla K20m wins the Vulkan test, but by a razor-thin margin. Its score of 21,936 edges out the AMD part’s 21,832 by just 0.5%, which is essentially a statistical tie in practical terms. This near-parity in Vulkan is striking because the two cards are built on completely different architectures, process nodes, and memory types. The data shows that the AMD Radeon Pro Vega 16 is the stronger OpenCL performer, while the NVIDIA Tesla K20m holds a marginal advantage in Vulkan, making the choice between them dependent on which API the target application uses.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The AMD Radeon Pro Vega 16 has an average benchmark score of 23,250, while the NVIDIA Tesla K20m averages 19,089. The AMD part sits at the 68th percentile, four points higher than the Tesla’s 64th percentile.

Q: How does the AMD Radeon Pro Vega 16 compare to its nearest rivals?

A: The database lists NVIDIA P106-100 as its closest competitor with an average score of 23,249, a delta of 0%. The AMD Radeon RX 6600M and Radeon R9 M290X are both 0.1% behind, and the Radeon AI PRO R9700 is 0.3% behind.

Q: What is the closest rival to the NVIDIA Tesla K20m?

A: The nearest rival is the NVIDIA GeForce RTX 4050 Mobile with an average score of 19,049, which is 0.2% higher than the Tesla. The AMD Radeon RX 6600 and NVIDIA Quadro K6000 are both 0.3% higher, while the NVIDIA GeForce GTX 780 is 0.4% lower.

Q: Which GPU has more memory?

A: The NVIDIA Tesla K20m has 5 GB of GDDR5 memory on a 320-bit bus, while the AMD Radeon Pro Vega 16 has 4 GB of HBM2 on a 1024-bit bus. The AMD part’s bandwidth is 307.2 GB/s versus the Tesla’s 208.0 GB/s.

Q: What is the TDP difference between the two cards?

A: The AMD Radeon Pro Vega 16 is rated at 75 W, while the NVIDIA Tesla K20m is rated at 225 W, a threefold difference in power draw.

Q: Which GPU has a higher FP32 throughput?

A: The NVIDIA Tesla K20m has 3.524 TFLOPS of FP32, while the AMD Radeon Pro Vega 16 has 2.437 TFLOPS. The Tesla is roughly 45% higher in raw FP32 compute.

Head-to-Head Benchmarks

The database records two head-to-head results, and each GPU claims one victory. In Geekbench OpenCL, the AMD Radeon Pro Vega 16 scores 18,268 against the Tesla K20m’s 16,241, a delta of 12.5% in favor of AMD. This is the larger margin of the two tests, and it reflects a real performance gap in OpenCL compute workloads. The AMD part achieves this despite having only 1,024 shading units compared to the Tesla’s 2,496, which suggests that memory bandwidth and architecture efficiency play a decisive role in this benchmark.

In Geekbench Vulkan, the tables turn slightly. The NVIDIA Tesla K20m scores 21,936 versus the AMD part’s 21,832, a delta of 0.5% in NVIDIA’s favor. This margin is small enough that run-to-run variance could plausibly flip the result, but the recorded data still gives the win to the Tesla. The Vulkan scores are much closer to each other than the OpenCL scores are, which indicates that the two GPUs are more evenly matched under this API. The AMD part’s Vulkan support is version 1.3, while the Tesla’s is 1.2.175, but the benchmark result does not show a clear advantage for the newer API revision.

The average benchmark scores reinforce the OpenCL result. The AMD Radeon Pro Vega 16 averages 23,250 across all tests, which is 21.8% higher than the Tesla K20m’s 19,089. This overall margin is driven almost entirely by the OpenCL win, since the Vulkan scores are nearly identical. The percentile ranking tells the same story: 68th for AMD versus 64th for NVIDIA, a four-point gap that places the Vega part in a higher overall tier.

Specification Differences

The two GPUs differ across nearly every specification field in the database. The AMD Radeon Pro Vega 16 uses a Vega 12 chip built on a 14 nm process at GlobalFoundries, while the NVIDIA Tesla K20m uses a GK110 chip on a 28 nm process at TSMC. The process node difference is significant, with AMD’s part being two generations newer in lithography. The Tesla K20m has 7,080 million transistors on a 561 mm² die, giving a transistor density of 12.6 million per square millimeter, while the AMD part’s transistor count and die size are not recorded.

Memory configurations are fundamentally different. The AMD part has 4 GB of HBM2 with a 1024-bit bus and 307.2 GB/s bandwidth, while the Tesla has 5 GB of GDDR5 with a 320-bit bus and 208.0 GB/s bandwidth. The AMD part’s bandwidth advantage is 47.7%, even though it has less capacity. Clock speeds also differ: the AMD part runs at a base of 815 MHz and a boost of 1190 MHz, while the Tesla’s base and boost clocks are not recorded in the database. The memory clock is 1200 MHz (2.4 Gbps effective) for AMD and 1300 MHz (5.2 Gbps effective) for NVIDIA.

The compute resources are lopsided. The Tesla has 2,496 shading units, 208 texture mapping units, and 40 ROPs, versus the AMD part’s 1,024 shading units, 64 TMUs, and 32 ROPs. The Tesla’s texture rate of 146.8 GTexel/s is nearly double the AMD part’s 76.16 GTexel/s, and its FP32 throughput of 3.524 TFLOPS is 44.6% higher than the AMD’s 2.437 TFLOPS. The pixel rates are closer: 36.71 GPixel/s for NVIDIA versus 38.08 GPixel/s for AMD, a 3.7% advantage for the Vega part.

Power and physical characteristics diverge sharply. The Tesla is a dual-slot card requiring 1x 6-pin and 1x 8-pin power connectors, with a 225 W TDP and a suggested 550 W power supply. The AMD part is an integrated GPU (IGP) with a 75 W TDP and no power connectors listed. The bus interface also differs: PCIe 3.0 x16 for AMD versus PCIe 2.0 x16 for NVIDIA. The Tesla has no display outputs, while the AMD part’s outputs are described as portable device dependent. The Tesla is 267 mm long, while the AMD part’s dimensions are not recorded.

Architecture Differences

The architectures are from entirely different design philosophies. The AMD Radeon Pro Vega 16 uses GCN 5.0, also known as Vega, which is a fifth-generation Graphics Core Next design. The NVIDIA Tesla K20m uses Kepler, which is a compute-focused architecture that predates modern NVIDIA designs. The process nodes reflect this generational gap: 14 nm for AMD versus 28 nm for NVIDIA, giving the AMD part a density advantage even though the die size is not recorded.

Memory architecture is a major differentiator. The AMD part uses HBM2, a high-bandwidth stacked memory technology that achieves 307.2 GB/s over a 1024-bit bus. The Tesla uses GDDR5 over a 320-bit bus, achieving 208.0 GB/s. This is a fundamental design choice: HBM2 trades capacity for bandwidth, while GDDR5 trades bandwidth for capacity. The Tesla’s 5 GB capacity is 25% higher than the AMD’s 4 GB, but the AMD part’s bandwidth is 47.7% higher.

The feature sets show different priorities. The AMD part supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3, while the Tesla supports DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175. The AMD part has a higher Vulkan revision, which may explain its near-parity in the Vulkan benchmark despite having fewer shaders. The Tesla has no FP16 support recorded, while the AMD part has 4.874 TFLOPS of FP16 at a 2:1 ratio. This makes the AMD part more flexible for mixed-precision workloads, even though the Tesla has higher absolute FP32 throughput.

Slot and power design reflect their intended use cases. The AMD part is an IGP, meaning it is integrated into a portable device and draws only 75 W. The Tesla is a dual-slot board with external power connectors, drawing 225 W and requiring a 550 W power supply. The AMD part’s display outputs are portable device dependent, while the Tesla has no display outputs at all, confirming its role as a dedicated compute accelerator rather than a graphics card.

The Verdict

The data points to a clear use-case split. For OpenCL compute workloads, the AMD Radeon Pro Vega 16 is the better choice, delivering 12.5% higher performance than the Tesla K20m. This is the larger benchmark margin in the database, and it comes with a 75 W TDP versus 225 W, making the AMD part far more power-efficient for the same measured performance. The AMD part’s higher average score (23,250 versus 19,089) and higher percentile (68 versus 64) reinforce this conclusion for general compute tasks.

For Vulkan workloads, the NVIDIA Tesla K20m has a slight edge, but the 0.5% margin is negligible in practice. The Tesla’s real advantage lies in raw FP32 throughput (3.524 TFLOPS versus 2.437 TFLOPS) and memory capacity (5 GB versus 4 GB), which are not directly measured in the head-to-head benchmarks but matter for specific applications. Users who need maximum FP32 compute or more memory capacity should choose the Tesla, despite its higher power draw and older architecture.

The AMD part is the better all-around choice based on the recorded benchmark data. It wins the larger benchmark, has a higher average score, and does so at one-third the power draw. The Tesla is a niche pick for workloads that require its specific compute resources, but the data does not show a scenario where its advantages translate into a decisive benchmark victory. The verdict is straightforward: choose AMD for OpenCL efficiency and overall benchmark performance, choose NVIDIA only if the Vulkan edge or raw compute resources are essential.