NVIDIA T400 4 GB vs NVIDIA Tesla K20m Comparison

Head-to-Head Benchmarks

The recorded data shows a split decision between these two NVIDIA workstation cards. In Geekbench OpenCL, the NVIDIA T400 4 GB takes the win with a score of 17,320 against the Tesla K20m's 16,241, a delta of 6.2% in favor of the T400. This is a notable result given the massive generational gap between the two architectures. The T400's OpenCL score places it in the 60th percentile of all GPUs in the database, while the K20m sits at the 64th percentile overall, meaning the K20m's average benchmark score across all tests is higher despite losing this specific test.

The Vulkan results flip the script dramatically. The Tesla K20m scores 21,936 in Geekbench Vulkan, which is 34.9% higher than the T400's 16,263. This is the single largest margin in either direction across the head-to-head tests. The K20m's Vulkan score is substantially above its own OpenCL score, while the T400 actually scores lower in Vulkan than it does in OpenCL. This suggests the K20m's compute-oriented design translates better to Vulkan's lower-level API access.

Looking at average benchmark scores, the K20m averages 19,089 across all recorded tests, while the T400 averages 16,792. That is a 2,297-point gap, or roughly 13.7% in favor of the older card. The K20m's nearest rivals in the database include the NVIDIA GeForce RTX 4050 Mobile at 19,049 (0.2% behind), the AMD Radeon RX 6600 at 19,036 (0.3% behind), the NVIDIA Quadro K6000 at 19,030 (0.3% behind), and the NVIDIA GeForce GTX 780 at 19,164 (0.4% ahead). The T400's nearest rivals are the AMD Radeon RX 7600S at 16,696 (0.6% behind), the NVIDIA Tesla M4 at 16,932 (0.8% ahead), the AMD Radeon HD 7970M at 17,019 (1.3% ahead), and the NVIDIA GeForce GTX 690 at 17,037 (1.4% ahead).

The win count is tied at one each, but the magnitude of the K20m's Vulkan victory dwarfs the T400's OpenCL advantage. The 34.9% Vulkan delta is more than five times larger than the 6.2% OpenCL delta, making the K20m the stronger overall performer in aggregate terms.

Where Each One Wins

The NVIDIA T400 4 GB wins in OpenCL workloads. This matters for applications that primarily use OpenCL for general-purpose compute, such as certain video encoding filters, physics simulations, and some scientific workloads. The T400's 6.2% advantage in this test, combined with its modern Turing architecture, makes it the better choice for OpenCL-centric tasks. The T400 also wins on power efficiency in practical terms: its 30 W TDP and lack of power connectors contrast sharply with the K20m's 225 W TDP and dual power connector requirement.

The NVIDIA Tesla K20m wins decisively in Vulkan. This is significant for any workload that leverages Vulkan's explicit control over GPU resources, including modern game engines, compute shaders, and increasingly some professional visualization tools. The 34.9% lead in this test is the dominant factor in the comparison. The K20m also carries more memory bandwidth (208.0 GB/s vs. 80.00 GB/s), more shading units (2,496 vs. 384), more texture mapping units (208 vs. 24), and more ROPs (40 vs. 16). Its FP32 throughput of 3.524 TFLOPS is more than three times the T400's 1,094.4 GFLOPS. For raw compute throughput, the K20m is the clear winner.

The T400 counters with its 12 nm process node versus the K20m's 28 nm node, giving it a transistor density of 23.5M per mm² compared to 12.6M per mm². The T400 also has a smaller die (200 mm² vs. 561 mm²) and fewer transistors (4,700 million vs. 7,080 million), which explains its drastically lower power draw. The T400 also supports DirectX 12 (12_1) while the K20m only supports DirectX 12 (11_0), and the T400 has a newer Vulkan version at 1.4 versus 1.2.175.

FAQ

Q: Which card has the higher average benchmark score?

A: The NVIDIA Tesla K20m, with an average score of 19,089 compared to the T400's 16,792. The K20m also sits at the 64th percentile of all GPUs, while the T400 sits at the 60th percentile.

Q: Why does the T400 beat the K20m in OpenCL despite having far fewer shading units?

A: The T400's Turing architecture is significantly newer, built on a 12 nm process versus the K20m's 28 nm. It also supports newer API versions, and its 1,094.4 GFLOPS FP32 throughput, while lower, is paired with faster effective memory (10 Gbps vs. 5.2 Gbps) and a modern driver stack. The OpenCL score of 17,320 reflects real-world performance that benefits from architectural improvements beyond raw compute counts.

Q: Is the K20m's Vulkan advantage large enough to matter in practice?

A: Yes. A 34.9% lead in Vulkan is substantial. The K20m scores 21,936 in Geekbench Vulkan versus 16,263 for the T400. For applications that use Vulkan for compute or rendering, this difference is highly noticeable.

Q: Which card has better memory bandwidth?

A: The Tesla K20m, with 208.0 GB/s over a 320-bit bus, compared to the T400's 80.00 GB/s over a 64-bit bus. The K20m also has more memory capacity at 5 GB versus 4 GB, though the T400 uses faster GDDR6 memory at 10 Gbps effective versus the K20m's GDDR5 at 5.2 Gbps.

Q: What are the power requirements for each card?

A: The K20m has a 225 W TDP, requires a 550 W suggested power supply, uses dual-slot cooling, and needs one 6-pin and one 8-pin power connector. The T400 has a 30 W TDP, requires a 200 W suggested power supply, is single-slot, and needs no power connectors at all.

Q: Which card supports newer graphics APIs?

A: The T400 supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. The K20m supports DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175. The T400 also has newer display outputs with three mini-DisplayPort 1.4a connectors, while the K20m has no display outputs.

Specification Differences

| Specification | Tesla K20m | T400 4 GB |

|---|---|---|

| Process Node | 28 nm | 12 nm |

| Transistors | 7,080 million | 4,700 million |

| Die Size | 561 mm² | 200 mm² |

| Transistor Density | 12.6M / mm² | 23.5M / mm² |

| Base Clock | Not listed | 420 MHz |

| Boost Clock | Not listed | 1425 MHz |

| Memory Clock | 1300 MHz, 5.2 Gbps effective | 1250 MHz, 10 Gbps effective |

| Memory Size | 5 GB | 4 GB |

| Memory Type | GDDR5 | GDDR6 |

| Memory Bus | 320 bit | 64 bit |

| Memory Bandwidth | 208.0 GB/s | 80.00 GB/s |

| Shading Units | 2,496 | 384 |

| TMUs | 208 | 24 |

| ROPs | 40 | 16 |

| Pixel Rate | 36.71 GPixel/s | 22.80 GPixel/s |

| Texture Rate | 146.8 GTexel/s | 34.20 GTexel/s |

| FP32 | 3.524 TFLOPS | 1,094.4 GFLOPS |

| FP16 | Not listed | 2.189 TFLOPS (2:1) |

| TDP | 225 W | 30 W |

| Slot Width | Dual-slot | Single-slot |

| Power Connectors | 1x 6-pin + 1x 8-pin | None |

| Suggested PSU | 550 W | 200 W |

| Bus Interface | PCIe 2.0 x16 | PCIe 3.0 x16 |

| Display Outputs | No outputs | 3x mini-DisplayPort 1.4a |

| DirectX Support | 12 (11_0) | 12 (12_1) |

| Vulkan Support | 1.2.175 | 1.4 |

| Release Date | 2013-01-04 | 2021-05-05 |

Architecture Differences

The Tesla K20m uses the GK110 chip based on the Kepler architecture, part of the Tesla Kepler generation. It is built on TSMC's 28 nm process with 7,080 million transistors packed into a 561 mm² die. This is a large, compute-oriented chip with 2,496 shading units, 208 TMUs, and 40 ROPs. Its memory subsystem uses 5 GB of GDDR5 on a 320-bit bus, delivering 208.0 GB/s. The K20m has no display outputs, reflecting its intended role as a dedicated compute accelerator. Its API support includes DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175.

The T400 4 GB uses the TU117 chip based on the Turing architecture, part of the Quadro Turing generation. It uses TSMC's 12 nm process with 4,700 million transistors on a 200 mm² die. This is a much smaller, more efficient chip with 384 shading units, 24 TMUs, and 16 ROPs. Its memory subsystem uses 4 GB of GDDR6 on a 64-bit bus, delivering 80.00 GB/s. The T400 includes three mini-DisplayPort 1.4a outputs, making it suitable for display workloads. It supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4.

The key architectural differences are the process node (28 nm vs. 12 nm), the compute scale (2,496 vs. 384 shading units), the memory bus width (320-bit vs. 64-bit), and the feature set. The K20m has no FP16 support listed, while the T400 offers 2.189 TFLOPS of FP16 performance. The T400's PCIe 3.0 interface is newer than the K20m's PCIe 2.0, though both use x16 lanes. The K20m's transistor density is 12.6M per mm², while the T400 achieves 23.5M per mm², reflecting the newer process technology.

The Verdict

The data supports two different use cases. For raw compute throughput, the Tesla K20m is the stronger card. Its 3.524 TFLOPS FP32 performance, 208.0 GB/s memory bandwidth, and 34.9% Vulkan lead over the T400 make it the better choice for compute-heavy workloads that can leverage its massive shading unit count. The K20m's average benchmark score of 19,089 places it in the 64th percentile, and its nearest rivals (RTX 4050 Mobile, RX 6600, Quadro K6000, GTX 780) all sit within 0.4% of its score, showing it remains competitive in aggregate performance.

For efficiency, display output, and modern API support, the T400 4 GB is the clear choice. Its 30 W TDP versus the K20m's 225 W TDP, its lack of power connectors, and its single-slot design make it dramatically easier to deploy in workstations. The T400 also wins the OpenCL test by 6.2%, supports newer DirectX and Vulkan versions, and includes display outputs for visualization tasks. Its 60th percentile standing and average score of 16,792 place it in a lower performance tier, but its efficiency and modern feature set are compelling for specific workloads.

The database records show a tie in wins (one each), but the magnitudes matter. The K20m's 34.9% Vulkan victory is the single most decisive result in this comparison, while the T400's OpenCL win is a modest 6.2%. Anyone needing Vulkan compute performance should choose the K20m. Anyone needing low power draw, display outputs, or modern API feature support should choose the T400. The K20m is end-of-life with a launch MSRP of 3,199 USD, while the T400 has no recorded launch MSRP, but the performance data alone tells a clear story: the older card still leads in raw compute, while the newer card wins on efficiency and platform compatibility.