AMD FirePro W7000 vs NVIDIA Tesla K80 Comparison

Head-to-Head Benchmarks

The benchmark data presents a split decision. In the Geekbench OpenCL test, the NVIDIA Tesla K80 takes the win with a score of 18,620 against the AMD FirePro W7000’s 17,808. That is a 4.4% margin in favor of the Tesla. This is a notable gap, but not a dominant one; the FirePro remains within striking distance in a compute-oriented workload. The K80’s advantage here aligns with its raw compute specifications, which are substantially higher on paper.

However, the story flips dramatically in the Geekbench Vulkan test. The AMD FirePro W7000 scores 22,001, while the NVIDIA Tesla K80 manages only 19,111. That is a 15.1% lead for the AMD card. This is a significant swing, and it suggests that the FirePro’s architecture handles Vulkan’s modern API features with considerably more efficiency than the Tesla’s older Kepler design. The data shows a clear trade-off: the Tesla leads in one API, but the FirePro dominates in the other by a much larger margin.

When looking at average benchmark scores, the FirePro W7000 lands at 19,905, placing it just ahead of the Tesla K80’s 18,866. The FirePro’s average is buoyed heavily by its strong Vulkan result. The Tesla’s average, meanwhile, reflects its more balanced but lower Vulkan performance. The overall percentile rankings reinforce this: the FirePro sits at the 65th percentile among all GPUs, while the Tesla sits at the 63rd. Both are mid-pack performers, but the FirePro holds a slight edge in aggregate standing.

The nearest rivals for each card provide additional context. The FirePro W7000’s average score of 19,905 is 0.1% ahead of the NVIDIA Tesla K40m (19,885), 0.7% ahead of the AMD Radeon RX 6650 XT (19,765), and 1.5% ahead of the NVIDIA Quadro K5200 (19,602). For the Tesla K80, its average of 18,866 is 0.4% ahead of the NVIDIA GeForce RTX 2070 (18,789), but 0.5% behind the NVIDIA RTX 2000 Ada Generation (18,954) and 0.9% behind both the NVIDIA Quadro K6000 (19,030) and the AMD Radeon RX 6600 (19,036). The FirePro’s rival set is tightly clustered, while the Tesla’s rivals show a slightly wider spread, with some newer cards edging it out.

Where Each One Wins

The use-case split is stark and driven by API preference. The NVIDIA Tesla K80 wins the Geekbench OpenCL benchmark outright. This is a compute-heavy test, and the Tesla’s architecture is clearly optimized for that kind of workload. Its higher shading unit count and memory bandwidth give it an edge when the task is purely about raw number-crunching in OpenCL. For users running scientific simulations, data analysis, or other OpenCL-centric applications, the data suggests the K80 is the stronger choice.

The AMD FirePro W7000, conversely, wins the Geekbench Vulkan benchmark by a wide 15.1% margin. Vulkan is a lower-level API that places more emphasis on driver efficiency and draw-call handling. The FirePro’s GCN 1.0 architecture, despite being older, appears to handle Vulkan’s command structures more fluidly. This makes the W7000 a better option for workloads that leverage Vulkan, such as certain modern game engines, compute shaders, or rendering pipelines that have moved to this API. The data implies that if your software stack is Vulkan-based, the FirePro is the clear winner here.

Beyond the head-to-head, the FirePro also has a slight aggregate advantage. Its average benchmark score is 5.5% higher than the Tesla’s. While neither card is a top-tier performer—both sit below the 70th percentile—the FirePro’s overall average suggests it is the more consistent of the two across different test types. The Tesla’s win in OpenCL is real, but it is narrow, whereas the FirePro’s Vulkan win is decisive. For a mixed workload environment, the FirePro appears to be the safer bet.

Architecture Differences

The architectural divide between these two cards is substantial. The AMD FirePro W7000 is built on the Pitcairn chip, using the GCN 1.0 architecture, and is part of the FirePro GCN (Wx000) generation. The NVIDIA Tesla K80 uses the GK210 chip with the Kepler 2.0 architecture, from the Tesla Kepler (Kxx) generation. Both are fabricated on a 28 nm process at TSMC, but the similarities end there.

The transistor counts tell a tale of scale. The Tesla’s GK210 packs 7,100 million transistors on a 561 mm² die, resulting in a transistor density of 12.7M per mm². The FirePro’s Pitcairn is much smaller, with 2,800 million transistors on a 212 mm² die, but it achieves a higher density of 13.2M per mm². This means the FirePro packs its transistors more tightly, even though it has far fewer of them overall. The Tesla’s larger die is dedicated to more compute units, but it is less dense.

The compute resources differ dramatically. The Tesla K80 has 2,496 shading units, 208 texture mapping units, and 48 render output units. The FirePro W7000 has 1,280 shading units, 80 TMUs, and 32 ROPs. The Tesla has roughly double the shading units and nearly triple the TMUs. This hardware advantage explains its higher pixel rate (42.85 GPixel/s vs. 30.40 GPixel/s) and texture rate (171.4 GTexel/s vs. 76.00 GTexel/s). The Tesla’s FP32 throughput is also much higher at 4.113 TFLOPS versus 2.432 TFLOPS.

Memory architecture also diverges. The Tesla K80 comes with 12 GB of GDDR5 on a 384-bit bus, delivering 240.6 GB/s of bandwidth. The FirePro W7000 has 4 GB of GDDR5 on a 256-bit bus, yielding 153.6 GB/s. The Tesla’s memory advantage is substantial, both in capacity and bandwidth. This is a key factor for large datasets that need to reside in VRAM. The FirePro’s smaller memory pool could be a bottleneck for certain workloads, despite its architectural efficiency in Vulkan.

Specification Differences

The specification sheets reveal several key differences beyond the compute cores. The clocks are a point of contrast. The Tesla K80 has a base clock of 562 MHz and a boost clock of 824 MHz, while the FirePro W7000’s base and boost clocks are not listed in the data. The memory clock also differs slightly: the Tesla runs at 1,253 MHz (5 Gbps effective), while the FirePro runs at 1,200 MHz (4.8 Gbps effective). These are modest differences, but they contribute to the overall performance gap.

Power consumption is a major differentiator. The Tesla K80 is rated at 300 W TDP, requiring a 700 W suggested PSU and a single 8-pin power connector. The FirePro W7000 is much more modest, with a 150 W TDP, a 450 W suggested PSU, and a single 6-pin connector. The Tesla draws twice the power, which has implications for system cooling and operating costs. The physical footprint also differs: the Tesla is a dual-slot card measuring 267 mm in length, while the FirePro is a single-slot card at 242 mm.

Display outputs are another clear split. The FirePro W7000 has four DisplayPort 1.2 outputs, making it a viable option for multi-monitor setups. The Tesla K80 has no display outputs at all, reinforcing its role as a compute-only accelerator. For any user needing to drive a display, the FirePro is the only choice here. The Tesla is purely for headless compute environments.

The API support is nearly identical, with both cards supporting DirectX 12 (11_1), OpenGL 4.6, and Vulkan. The Tesla has a slightly newer Vulkan version (1.2.175) compared to the FirePro’s 1.2.170, but this is a trivial difference in practice. The release dates are also far apart: the FirePro launched on June 12, 2012, while the Tesla launched on November 16, 2014. Both are end-of-life products now, but the Tesla is the newer design by over two years.

FAQ

Q: Which card has a higher average benchmark score?

A: The AMD FirePro W7000 has an average benchmark score of 19,905, which is higher than the NVIDIA Tesla K80’s 18,866. This puts the FirePro at the 65th percentile versus the Tesla’s 63rd.

Q: How much faster is the NVIDIA Tesla K80 in OpenCL?

A: The Tesla K80 scores 18,620 in Geekbench OpenCL, which is 4.4% higher than the FirePro W7000’s 17,808. The Tesla wins this specific test.

Q: What is the biggest performance gap between the two cards?

A: The largest gap is in Geekbench Vulkan, where the AMD FirePro W7000 scores 22,001 compared to the Tesla K80’s 19,111. This represents a 15.1% lead for the FirePro.

Q: Does the NVIDIA Tesla K80 have display outputs?

A: No, the Tesla K80 has no display outputs. It is a compute-only card. The AMD FirePro W7000, in contrast, has four DisplayPort 1.2 outputs.

Q: What is the memory capacity difference?

A: The NVIDIA Tesla K80 has 12 GB of GDDR5 memory, while the AMD FirePro W7000 has 4 GB. The Tesla also has a wider 384-bit bus, providing 240.6 GB/s bandwidth versus the FirePro’s 153.6 GB/s.

Q: Which card requires more power?

A: The NVIDIA Tesla K80 has a 300 W TDP and requires a 700 W suggested PSU. The AMD FirePro W7000 has a 150 W TDP and a 450 W suggested PSU.

The Verdict

The data points to a clear choice based on workload. For compute-heavy tasks that rely on OpenCL, the NVIDIA Tesla K80 is the stronger option. Its 4.4% lead in that benchmark, combined with its higher FP32 throughput (4.113 TFLOPS), larger memory capacity (12 GB), and greater bandwidth (240.6 GB/s), makes it the preferred card for scientific computing or data processing. The Tesla’s higher TDP of 300 W is a trade-off, but the performance headroom is there.

For users working with Vulkan-based applications, the AMD FirePro W7000 is the definitive winner. Its 15.1% lead in that benchmark is decisive, and its average score is 5.5% higher overall. The FirePro also offers display outputs, a lower power draw (150 W), and a smaller single-slot footprint. If your software stack is Vulkan-centric, or if you need a card that can drive a monitor, the FirePro is the logical pick.

Neither card is a top-tier performer today—both are end-of-life products sitting at or below the 65th percentile. But the choice between them is not a matter of one being universally better. It depends entirely on the API and workload profile. The Tesla K80 is a compute workhorse with a narrow OpenCL edge and massive memory. The FirePro W7000 is a more balanced, efficient card that excels in Vulkan and offers display functionality. Choose based on the benchmarks that matter to your specific use case.