GPU Comparison

The NVIDIA Quadro K5100M and NVIDIA Tesla C2070 represent two very different generations of NVIDIA’s professional GPU lineup, and the benchmark data shows a clear, decisive winner in compute workloads. Based on the available Geekbench OpenCL results, the Quadro K5100M delivers a 21.2% higher score (11,771 vs. 9,716) than the Tesla C2070, making it the superior choice for general-purpose compute tasks despite the Tesla’s legacy as a dedicated compute card. The data shows a single head-to-head benchmark, and the Quadro K5100M wins that matchup outright, reinforcing its advantage in modern driver optimization and architecture efficiency.

Head-to-Head Benchmarks

The only direct comparison available in the data is the Geekbench OpenCL test, which measures raw compute throughput across a variety of workloads. The Quadro K5100M scores 11,771, while the Tesla C2070 trails significantly at 9,716. This represents a 21.2% performance advantage for the Quadro, a substantial margin that cannot be dismissed as a minor architectural tick. The Tesla C2070’s score places it in the 47th percentile of all GPUs, while the Quadro K5100M sits at the 48th percentile, a narrow overall ranking difference, but the raw score gap tells a more compelling story.

What makes this result particularly striking is the hardware disparity. The Tesla C2070 features a wider 384-bit memory bus and higher peak bandwidth (143.4 GB/s vs. 115.2 GB/s), yet it still loses decisively. The Quadro’s advantage comes from its newer Kepler architecture, which extracts more usable performance from fewer resources. The Tesla’s Fermi design, with 448 shading units, cannot compete with the Quadro’s 1,536 shading units in modern OpenCL implementations. The Quadro also benefits from a much higher texture rate (98.69 GTexel/s vs. 32.14 GTexel/s) and pixel rate (24.67 GPixel/s vs. 16.07 GPixel/s), which likely contribute to its stronger overall compute showing.

When placed against their respective nearest rivals, the picture becomes clearer. The Quadro K5100M’s average benchmark score of 10,043 puts it within 0.3% of the AMD Radeon R9 M375 (10,070) and just 0.3% ahead of the AMD Radeon Pro 5300M (10,013). It also edges out the NVIDIA GeForce GTX 870M by 0.8% and the NVIDIA Quadro 6000 by 2%. The Tesla C2070, by contrast, posts an average score of 9,716, which is 0.1% behind the NVIDIA Tesla M10 (9,724) and 0.7% behind the AMD Radeon Pro WX 2100 (9,653). Interestingly, the Tesla is 0.7% behind the NVIDIA GeForce GTX 1070 (9,780), a consumer card that far outclasses it in raw compute. These rival comparisons underscore that the Quadro K5100M is not just faster than the Tesla, it is competitive with a broader range of contemporaneous and newer GPUs, whereas the Tesla lags even modest workstation parts.

FAQ

Q: Which GPU is faster in OpenCL compute workloads?

A: The NVIDIA Quadro K5100M is decisively faster, scoring 11,771 versus the Tesla C2070’s 9,716 in Geekbench OpenCL, a 21.2% delta in favor of the Quadro.

Q: Does the Tesla C2070 have any benchmark win over the Quadro K5100M?

A: No. The data shows zero wins for the Tesla C2070 in the head-to-head comparison. The Quadro K5100M wins the only available benchmark (Geekbench OpenCL) with a 1–0 record.

Q: How do these GPUs compare to their nearest rivals?

A: The Quadro K5100M’s average score (10,043) is nearly identical to the AMD Radeon R9 M375 (10,070, -0.3%) and slightly ahead of the AMD Radeon Pro 5300M (10,013, +0.3%). The Tesla C2070’s average (9,716) is marginally behind the NVIDIA Tesla M10 (9,724, -0.1%) and slightly ahead of the NVIDIA Quadro P4000 (9,665, +0.5%).

Q: What are the memory specifications of each card?

A: The Quadro K5100M has 8 GB of GDDR5 on a 256-bit bus with 115.2 GB/s bandwidth. The Tesla C2070 has 6 GB of GDDR5 on a 384-bit bus with 143.4 GB/s bandwidth.

Q: Which card has a higher transistor count?

A: The Quadro K5100M packs 3,540 million transistors on a 294 mm² die, while the Tesla C2070 has 3,100 million transistors on a much larger 529 mm² die.

Q: What is the power consumption difference?

A: The Quadro K5100M has a 100 W TDP and uses an MXM module slot with no power connectors. The Tesla C2070 has a 238 W TDP, requires a dual-slot form factor, and needs both a 6-pin and 8-pin power connector, with a suggested 550 W PSU.

The Verdict

The data is unambiguous: the NVIDIA Quadro K5100M is the better GPU for compute performance. It wins the only head-to-head benchmark by 21.2%, and its average benchmark score (10,043) exceeds the Tesla C2070’s (9,716) by roughly 3.4%. The Quadro also posts a higher percentile ranking (48th vs. 47th), confirming that it sits marginally higher in the overall GPU distribution. For any application relying on OpenCL compute, the Quadro K5100M is the clear pick.

The Tesla C2070 does retain some theoretical advantages in memory bandwidth (143.4 GB/s vs. 115.2 GB/s) and bus width (384-bit vs. 256-bit), but these do not translate into real-world compute wins in the available data. Its older Fermi architecture and lower shading unit count (448 vs. 1,536) are simply too large a deficit to overcome. The Tesla also consumes more than twice the power (238 W vs. 100 W) and requires a bulkier dual-slot physical footprint with external power connectors, making it a less practical option in most deployment scenarios.

For users deciding between these two, the Quadro K5100M is the superior choice for any compute-heavy workload. The Tesla C2070 might still be considered if legacy driver support or a specific PCIe 2.0 x16 form factor is required, but the performance data offers no justification for choosing it over the Quadro on merit alone.

Specification Differences

The two cards differ across nearly every major specification category. The Quadro K5100M uses a 28 nm process, while the Tesla C2070 uses a 40 nm process. Transistor counts are close, 3,540 million vs. 3,100 million, but the die sizes are very different: the Quadro is 294 mm², while the Tesla is a massive 529 mm². This yields a transistor density of 12.0M / mm² for the Quadro versus just 5.9M / mm² for the Tesla, highlighting the Quadro’s manufacturing advantage.

Memory configurations also diverge sharply. The Quadro has 8 GB of GDDR5 on a 256-bit bus, delivering 115.2 GB/s. The Tesla has 6 GB of GDDR5 on a 384-bit bus, delivering 143.4 GB/s. Clock speeds are difficult to compare directly, as the Tesla’s base and boost clocks are not listed, while the Quadro runs at 771 MHz base and boost. The memory clock is 900 MHz (3.6 Gbps effective) for the Quadro and 747 MHz (3 Gbps effective) for the Tesla.

Compute resources strongly favor the Quadro: 1,536 shading units, 128 TMUs, and 32 ROPs versus the Tesla’s 448 shading units, 56 TMUs, and 48 ROPs. Pixel rate is 24.67 GPixel/s vs. 16.07 GPixel/s, and texture rate is 98.69 GTexel/s vs. 32.14 GTexel/s. FP32 performance is 2.369 TFLOPS for the Quadro versus 1,027.7 GFLOPS for the Tesla. The TDP is 100 W vs. 238 W, and the form factors are MXM Module vs. Dual-slot. The Quadro has no power connectors, while the Tesla requires 1x 6-pin + 1x 8-pin and a suggested 550 W PSU. The bus interface is MXM-B (3.0) vs. PCIe 2.0 x16, and the Quadro’s display outputs are portable-device dependent, while the Tesla offers a single DVI output.

Architecture Differences

The architectural divide is generational. The Quadro K5100M is built on the Kepler architecture (chip GK104), while the Tesla C2070 uses the older Fermi architecture (chip GF100). Both are manufactured by TSMC, but at different nodes: 28 nm for Kepler and 40 nm for Fermi. The Kepler design is far more efficient, packing 3,540 million transistors into a 294 mm² die, whereas Fermi requires 529 mm² for 3,100 million transistors, a direct reflection of the process node advantage.

The shading unit count is the most dramatic difference: 1,536 on the Quadro versus 448 on the Tesla. This 3.4x ratio explains the Quadro’s dominant FP32 throughput. Texture units also favor the Quadro (128 vs. 56), though the Tesla has more ROPs (48 vs. 32), which helps its pixel output. The Quadro’s 2.369 TFLOPS FP32 performance is more than double the Tesla’s 1,027.7 GFLOPS, a decisive gap for compute tasks.

Memory architecture differs as well. The Tesla’s 384-bit bus versus the Quadro’s 256-bit bus gives the older card higher peak bandwidth, but the Quadro’s newer memory controller and higher effective clock (3.6 Gbps vs. 3 Gbps) partially close the gap. Neither card supports FP16, ray tracing, or tensor cores, both are pure compute/rendering designs from their respective eras. The API support is similar for DirectX (12 (11_0)) and OpenGL (4.6), but the Quadro adds Vulkan 1.2.175 support, while the Tesla has none listed.

Where Each One Wins

The Quadro K5100M wins in every measurable compute category. Its OpenCL score is 21.2% higher, its FP32 throughput is more than double, and its texture and pixel rates are substantially higher. It is also significantly more power-efficient (100 W vs. 238 W) and uses a compact MXM Module form factor, making it suitable for portable workstations and systems where space and cooling are constrained. The 8 GB memory capacity also gives it an edge for larger datasets that fit within a single GPU’s VRAM.

The Tesla C2070’s only theoretical advantages are its wider 384-bit memory bus and higher peak bandwidth (143.4 GB/s). For workloads that are extremely memory-bandwidth-bound, such as certain sparse linear algebra or large streaming operations, the Tesla might offer a marginal benefit, but the available benchmark data does not confirm this. Its PCIe 2.0 x16 interface is a standard desktop slot, which could be an advantage if the target system cannot accommodate an MXM module. The Tesla also has a single DVI output, which is more than the Quadro’s portable-device-dependent outputs if a direct display connection is required. However, given the substantial compute deficit and much higher power draw, these niche advantages do not outweigh the Quadro’s clear performance leadership. For most users, the Quadro K5100M is the definitive choice.