AMD FirePro W5170M vs NVIDIA Tesla C2075 Comparison

Head-to-Head Benchmarks

The database contains one directly comparable workload between these two cards: Geekbench OpenCL. The NVIDIA Tesla C2075 scores 10,400 points, while the AMD FirePro W5170M scores 8,140 points. That gives the Tesla a 27.8% lead in raw compute throughput, a decisive margin for any OpenCL-based task such as rendering, simulation, or general GPU compute.

The Tesla C2075's advantage is substantial enough to place it in a different performance tier. Its nearest rivals in the database include the AMD Radeon RX 6500M (10,362, just 0.4% behind), the NVIDIA GeForce GTX 950A (10,273, 1.2% behind), and the AMD Radeon RX 550X (10,481, 0.8% ahead). The FirePro W5170M, by contrast, sits near the AMD Radeon HD 8870M (8,462, 1.6% behind) and the NVIDIA GeForce MX330 (8,458, 1.6% behind). In practical terms, the Tesla C2075 is roughly 21% faster than the FirePro's nearest rival cluster, which underscores how far apart these two parts are in raw OpenCL performance.

The FirePro W5170M has a second recorded benchmark, Geekbench Vulkan, scoring 9,050 points. The Tesla C2075 has no Vulkan score in the database, so a direct comparison there is not possible. However, the FirePro's Vulkan result is 11.2% higher than its OpenCL score, indicating that its GCN architecture handles Vulkan workloads somewhat more efficiently than OpenCL. The Tesla, lacking Vulkan support entirely, cannot contest that metric.

Overall, the head-to-head data shows one clear winner in the shared benchmark. The Tesla C2075 wins the only comparable test, and it does so by a wide margin. The FirePro W5170M's additional Vulkan score does not offset that deficit, as it represents a different API and a different workload profile.

FAQ

Q: Which GPU is faster in OpenCL compute?

A: The NVIDIA Tesla C2075, with a Geekbench OpenCL score of 10,400 versus 8,140 for the AMD FirePro W5170M, a 27.8% difference.

Q: Does the AMD FirePro W5170M support Vulkan?

A: Yes, it supports Vulkan 1.2.170 and records a Geekbench Vulkan score of 9,050. The NVIDIA Tesla C2075 has no Vulkan support listed in the database.

Q: How does the Tesla C2075 compare to its closest rivals?

A: It sits within 1.7% of the AMD Radeon R9 M275X (10,582), AMD Radeon RX 550X (10,481), AMD Radeon RX 6500M (10,362), and NVIDIA GeForce GTX 950A (10,273). Its 48th percentile among all GPUs reflects this mid-pack positioning.

Q: How does the FirePro W5170M compare to its closest rivals?

A: Its average score of 8,595 places it within 1.6% of the NVIDIA Quadro P2200 (8,686), Intel Arc A380 (8,558), AMD Radeon HD 8870M (8,462), and NVIDIA GeForce MX330 (8,458). Its 44th percentile ranking is slightly below the Tesla's.

Q: What memory configurations do the two cards use?

A: The Tesla C2075 has 6 GB of GDDR5 on a 384-bit bus with 150.3 GB/s bandwidth. The FirePro W5170M has 2 GB of GDDR5 on a 128-bit bus with 72.00 GB/s bandwidth.

Q: Which card has a higher pixel fill rate?

A: The Tesla C2075, at 16.07 GPixel/s versus 14.80 GPixel/s for the FirePro W5170M.

Architecture Differences

The two GPUs come from different architectural generations and design philosophies. The NVIDIA Tesla C2075 uses the GF110 chip, built on Fermi 2.0 architecture, fabricated on a 40 nm process at TSMC. It packs 3,000 million transistors onto a 520 mm² die, yielding a transistor density of 5.8 million per mm². The AMD FirePro W5170M uses the Tropo chip, based on GCN 1.0 architecture, also from TSMC but on a 28 nm process. It contains 1,500 million transistors on a much smaller 123 mm² die, giving a transistor density of 12.2 million per mm².

These differences are stark. The Tesla's Fermi architecture is a compute-first design with 448 shading units, 56 texture mapping units, and 48 ROPs. The FirePro's GCN architecture is a graphics-first design with 640 shading units, 40 TMUs, and only 16 ROPs. Despite having fewer shading units, the Tesla achieves higher pixel throughput (16.07 GPixel/s vs 14.80 GPixel/s) thanks to its much wider ROP count. In texture rate, the FirePro wins: 37.00 GTexel/s versus 32.14 GTexel/s for the Tesla, a result of its higher clock speeds.

The FirePro W5170M has a base clock of 900 MHz and a boost clock of 925 MHz, while the Tesla C2075 lists no base or boost clocks in the database, only a memory clock of 783 MHz (3.1 Gbps effective). The FirePro's memory runs at 1125 MHz (4.5 Gbps effective). The Tesla's memory clock is lower, but its 384-bit bus width more than compensates, delivering 150.3 GB/s versus 72.00 GB/s for the FirePro. That bandwidth advantage is critical for large compute workloads.

Architecture-level feature support also differs. The Tesla supports DirectX 12 (11_0) and OpenGL 4.6, with no Vulkan listed. The FirePro supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.170. The FirePro's newer GCN architecture and 28 nm process give it access to modern API features, while the Tesla's Fermi design, despite its age, retains strong compute throughput in OpenCL.

The FirePro's higher transistor density (12.2M / mm² versus 5.8M / mm²) reflects the generational jump in process technology. The Tesla's larger die and higher transistor count were necessary to achieve its compute performance on an older node, but that approach also explains its 247 W TDP and dual-slot design. The FirePro, as a mobile MXM module, draws no listed TDP and uses no external power connectors, making it a far more power-efficient design per transistor.

Specification Differences

The two cards differ across nearly every specification category. The Tesla C2075 is a desktop-oriented card from 2011, while the FirePro W5170M is a mobile workstation part from 2014.

• Process node: 40 nm (Tesla) vs 28 nm (FirePro)
• Transistors: 3,000 million vs 1,500 million
• Die size: 520 mm² vs 123 mm²
• Transistor density: 5.8M / mm² vs 12.2M / mm²
• Memory size: 6 GB vs 2 GB
• Memory bus width: 384 bit vs 128 bit
• Memory bandwidth: 150.3 GB/s vs 72.00 GB/s
• Shading units: 448 vs 640
• TMUs: 56 vs 40
• ROPs: 48 vs 16
• Pixel rate: 16.07 GPixel/s vs 14.80 GPixel/s
• Texture rate: 32.14 GTexel/s vs 37.00 GTexel/s
• FP32 compute: 1,027.7 GFLOPS vs 1,184.0 GFLOPS
• TDP: 247 W vs not listed
• Slot width: Dual-slot vs MXM Module
• Power connectors: 1x 6-pin + 1x 8-pin vs None
• Bus interface: PCIe 2.0 x16 vs MXM-A (3.0)
• Display outputs: 1x DVI vs Portable Device Dependent
• Vulkan support: None vs 1.2.170
• DirectX support: 12 (11_0) vs 12 (11_1)
• Release date: July 2011 vs August 2014

The Tesla's higher FP32 peak (1,184.0 GFLOPS) belongs to the FirePro, not the Tesla. The Tesla's 1,027.7 GFLOPS is lower, yet it still wins the OpenCL benchmark by 27.8%. This suggests that the Tesla's memory bandwidth and ROP configuration contribute more to real-world OpenCL performance than raw FP32 throughput alone.

Where Each One Wins

The NVIDIA Tesla C2075 wins in the only directly comparable benchmark, Geekbench OpenCL, by 27.8%. It also holds advantages in memory capacity (6 GB vs 2 GB), memory bandwidth (150.3 GB/s vs 72.00 GB/s), pixel rate (16.07 GPixel/s vs 14.80 GPixel/s), and ROP count (48 vs 16). These traits make it better suited for large compute tasks where memory capacity and bandwidth are limiting factors, such as scientific simulation, data processing, or rendering workloads that need to keep large datasets resident on the GPU.

The AMD FirePro W5170M wins in texture rate (37.00 GTexel/s vs 32.14 GTexel/s), FP32 compute (1,184.0 GFLOPS vs 1,027.7 GFLOPS), shading unit count (640 vs 448), and Vulkan support (1.2.170 vs none). It also has a much smaller die (123 mm² vs 520 mm²) and lower power draw, as indicated by its lack of external power connectors and MXM form factor. These traits make it better suited for mobile workstation use cases where power efficiency and modern API support matter more than raw memory bandwidth.

The FirePro's Vulkan score of 9,050 shows it can handle modern graphics workloads, and its newer architecture gives it access to features the Fermi-based Tesla cannot offer. However, in the shared OpenCL test, the Tesla's older architecture still outperforms it by a wide margin, indicating that compute performance on Fermi was well optimized for OpenCL.

For users prioritizing raw compute throughput and memory bandwidth, the Tesla C2075 is the clear choice. For users needing a mobile form factor, Vulkan support, and lower power consumption, the FirePro W5170M is the only option that fits.

The Verdict

The data points to one conclusion: the NVIDIA Tesla C2075 is the stronger performer in OpenCL compute, beating the AMD FirePro W5170M by 27.8% in the only head-to-head benchmark. Its 6 GB memory, 150.3 GB/s bandwidth, and 48 ROPs give it a substantial advantage in memory-bound workloads. The FirePro W5170M cannot match that, despite having a newer architecture, higher FP32 peak, and more shading units.

That said, the FirePro W5170M is not without merit. It offers Vulkan support, a mobile MXM form factor, no external power connectors, and a much smaller die (123 mm² vs 520 mm²). It is a product from a different era and a different segment: mobile workstations versus desktop compute accelerators. The Tesla C2075 demands a dual-slot chassis, a 550 W suggested PSU, and 1x 6-pin + 1x 8-pin power connectors. The FirePro needs none of that.

The choice comes down to workload and platform. If the task is OpenCL compute on a desktop system with ample power and space, the Tesla C2075 is the better part, and its 48th percentile ranking among all GPUs reflects a solid mid-range compute capability. If the task is mobile workstation graphics with modern API support, the FirePro W5170M is the only viable option, despite its lower 44th percentile ranking.

The Tesla's end-of-life status and 2011 release date mean it is a legacy product, but the benchmark data shows it still outperforms the 2014 FirePro in the one test they share. The FirePro's successor, Radeon Pro Mobile, suggests AMD moved on from this design, while the Tesla's successor, Tesla Kepler, indicates NVIDIA also progressed. For anyone comparing these two specific cards today, the Tesla C2075 wins on compute, while the FirePro W5170M wins on efficiency and portability. Choose accordingly.