NVIDIA Tesla C2075

The NVIDIA Tesla C2075 is a professional compute card from the Fermi 2.0 generation, built on a 40 nm process at TSMC with 3,000 million transistors on a 520 mm² die. It targets a niche where raw compute throughput matters more than modern feature support, and its benchmark data reflects a product that remains competitive in specific OpenCL workloads despite its age.

Benchmark Performance

The Tesla C2075’s only recorded benchmark is Geekbench OpenCL, where it scores 10,563 points. This places it at the 48th percentile of all GPUs, meaning roughly half of the tested graphics cards outperform it in this specific compute test. The score itself is not exceptional, but the context of its nearest rivals reveals a tightly packed field.

The closest competitor is the AMD Radeon RX 6500M, which scores 10,589 points. The Tesla C2075 trails by only 0.2%, a margin so small it falls within typical run-to-run variance. Next is the AMD Radeon RX 6600S at 10,625 points, where the Tesla is 0.6% behind. These two AMD mobile parts are much newer and feature dedicated ray tracing hardware, yet they barely edge out a 2011 compute card in OpenCL.

The NVIDIA GeForce GTX 560 Ti scores 10,715 points, putting the Tesla 1.4% behind. This is notable because the GTX 560 Ti is a consumer gaming card from the same Fermi generation, sharing the same underlying architecture principles. The Tesla C2075’s 448 shading units and 1,027.7 GFLOPS FP32 throughput are its primary drivers here, and the data shows that raw shader count still matters for OpenCL. The NVIDIA Quadro K2200, a professional workstation card, scores 10,762 points, leaving the Tesla 1.8% behind. The K2200 benefits from a newer architecture generation, but the performance gap is surprisingly narrow.

What the data implies is that the Tesla C2075’s compute capability is not obsolete for OpenCL tasks. It sits within 2% of four different rivals, spanning mobile and desktop, consumer and professional segments. The benchmark results indicate that for pure compute workloads that scale with shading units and FP32 throughput, this card can still hold its own against much newer hardware. However, the 48th percentile also signals that half the GPU population is faster, so it is not a top-tier performer by any modern standard.

Power and Cooling

The Tesla C2075 has a TDP of 247 W, which is modest for a dual-slot compute card of its era. The suggested power supply is 550 W, and the card requires 1x 6-pin plus 1x 8-pin power connectors. This dual-connector setup is typical for Fermi-generation professional cards, which drew significant power for their compute arrays.

The 40 nm process and 3,000 million transistors contribute to this power draw, and the 247 W TDP means the card will generate substantial heat under sustained load. The dual-slot cooling solution is designed to handle this, expelling hot air directly from the chassis. The card’s length of 248 mm (9.8 inches) means it will fit in most mid-tower cases, but the power connector requirement and TDP should be the primary considerations for system integration. For a compute workload running continuously, a 550 W PSU is the minimum recommendation, and users should ensure their power supply has the correct connectors available. The data does not suggest any headroom for overclocking or power tuning, so the 247 W figure should be treated as a fixed operational parameter.

Ray Tracing and Feature Set

The Tesla C2075 has no dedicated ray tracing cores and no tensor cores. This is a pure compute card from the Fermi 2.0 architecture, which predates hardware-accelerated ray tracing by several generations. Its feature set is defined by its 448 shading units, 56 texture mapping units, and 48 raster output units, which deliver a pixel rate of 16.07 GPixel/s and a texture rate of 32.14 GTexel/s.

In terms of API support, the card supports DirectX 12 (11_0) and OpenGL 4.6. The DirectX 12 support is at the 11_0 feature level, which means it lacks the full DirectX 12 feature set found on modern GPUs. There is no Vulkan support listed, which limits its compatibility with modern cross-platform graphics APIs. The single display output is 1x DVI, confirming that this is not designed for gaming or visual output but for headless compute servers or workstation environments where the GPU is accessed remotely.

The absence of ray tracing and tensor cores means the Tesla C2075 cannot accelerate modern AI workloads or ray-traced rendering. Its FP32 throughput of 1,027.7 GFLOPS is its primary compute asset, and benchmark results show this is sufficient for OpenCL tasks but not for the specialized workloads that require tensor operations. The feature set is firmly rooted in the Fermi era, and the data shows no path to modern feature support without a hardware upgrade.

FAQ

Q: What is the Tesla C2075’s benchmark score?

A: The Tesla C2075 scores 10,563 points on the Geekbench OpenCL benchmark, placing it at the 48th percentile of all GPUs.

Q: How does it compare to the AMD Radeon RX 6500M?

A: The AMD Radeon RX 6500M scores 10,589 points, which is 0.2% higher than the Tesla C2075.

Q: What memory configuration does the Tesla C2075 use?

A: It has 6 GB of GDDR5 memory on a 384-bit bus, delivering 150.3 GB/s of bandwidth with a memory clock of 783 MHz (3.1 Gbps effective).

Q: Does the Tesla C2075 support hardware ray tracing?

A: No, it has no ray tracing cores and no tensor cores, as it is based on the Fermi 2.0 architecture.

Q: What power supply is recommended for this card?

A: The suggested PSU is 550 W, and the card requires 1x 6-pin and 1x 8-pin power connectors.

Q: What APIs are supported?

A: The card supports DirectX 12 (11_0) and OpenGL 4.6, with no Vulkan support.

How It Compares

Against the AMD Radeon RX 6500M, the Tesla C2075 is virtually tied in OpenCL performance, trailing by just 0.2%. This is remarkable given the RX 6500M is a modern mobile GPU with a much smaller die and newer process node. The data suggests that for compute tasks, the Tesla’s larger shader count compensates for its architectural age.

The AMD Radeon RX 6600S is 0.6% ahead, and this margin is still negligible in practical terms. The RX 6600S likely draws less power and supports newer features, but the benchmark shows the Tesla C2075 remains competitive in raw OpenCL throughput. This comparison highlights that compute performance does not always scale with generational improvements.

The NVIDIA GeForce GTX 560 Ti is 1.4% faster, which is expected since both share the Fermi architecture. The GTX 560 Ti is a consumer card with similar compute characteristics, but the Tesla C2075’s 6 GB VRAM gives it a capacity advantage for large datasets. The performance gap is small, but the Tesla’s professional positioning suggests it was designed for sustained compute rather than gaming.

The NVIDIA Quadro K2200 leads by 1.8%, the largest delta among the nearest rivals. The K2200 is a newer professional card, and its higher score likely comes from architectural efficiency improvements. Still, the Tesla C2075 is within striking distance, meaning that for OpenCL workloads, the older card does not embarrass itself. The data shows a tight cluster of four rivals, all within 2% of each other, with the Tesla C2075 at the lower end but not isolated.

Memory Subsystem

The Tesla C2075 features 6 GB of GDDR5 memory on a 384-bit bus, with a memory clock of 783 MHz (3.1 Gbps effective). This configuration yields a bandwidth of 150.3 GB/s. For its era, 6 GB was a substantial amount of VRAM, and it remains useful for compute workloads that require large in-memory datasets.

The 384-bit bus width is wide, which helps sustain the 150.3 GB/s bandwidth even at the relatively modest memory clock. In high-resolution rendering or large data processing, this bandwidth is the limiting factor more than the raw FP32 throughput. The pixel rate of 16.07 GPixel/s and texture rate of 32.14 GTexel/s are tied to the memory subsystem’s ability to feed the shading units.

At 1080p or 1440p gaming resolutions, 6 GB is adequate, but the card is not designed for gaming. The data suggests that for compute tasks like scientific simulation or data analysis, the 6 GB capacity is the primary asset, while the 150.3 GB/s bandwidth ensures the 448 shading units are kept busy. Compared to modern cards with higher bandwidth, the Tesla C2075’s memory is a bottleneck, but within its own generation, it was a well-balanced configuration.

Who Should Consider It

The Tesla C2075 is for users who run OpenCL-based compute workloads and need large memory capacity. With 6 GB VRAM and a 48th percentile benchmark score, it is suitable for tasks that fit within that memory envelope and do not require modern features like ray tracing or tensor cores. The 1,027.7 GFLOPS FP32 throughput is competitive with its nearest rivals, all of which score within 2% of the Tesla in OpenCL.

For 1080p compute tasks, the card is sufficient, but the lack of Vulkan support and limited DirectX 12 feature level restrict its use in modern graphics applications. The 247 W TDP and 550 W PSU requirement mean it is not a low-power option, but for a dedicated compute server, this is acceptable. The end-of-life production status indicates that new units are not available, so this is a card for existing systems or second-hand purchases.

Users who rely on CUDA or OpenCL for medical imaging, finite element analysis, or similar fields may find the Tesla C2075 adequate, provided their software does not require newer APIs. The benchmark data shows it performs on par with the AMD Radeon RX 6500M and RX 6600S, which are modern parts, so for pure compute density, the Tesla is not obsolete. However, anyone needing ray tracing, tensor operations, or modern API support should look elsewhere. The card is best suited for legacy compute environments where the 6 GB memory capacity and 448 shading units are still relevant.