GPU Comparison

The benchmark data shows a clear overall victory for the NVIDIA Quadro K5000, which wins both head-to-head tests against the NVIDIA Tesla M10. While the Tesla M10 is a newer Maxwell-based card, the Quadro K5000’s Kepler architecture delivers superior raw compute performance in the available benchmarks, despite its older release date and lower average score across the broader database.

Head-to-Head Benchmarks

The Quadro K5000 dominates the Tesla M10 in both shared benchmark tests, but the margin of victory is substantial in one and moderate in the other. In Geekbench OpenCL, the Quadro K5000 scores 11,418 versus the Tesla M10’s 10,318, a lead of 9.6%. This is a significant gap, placing the K5000 in a different performance tier for compute workloads that leverage OpenCL. The Vulkan test shows an even larger disparity, with the Quadro K5000 scoring 11,169 against the Tesla M10’s 9,130, a commanding 18.3% advantage. This indicates that the K5000 is not just slightly faster but decisively ahead in graphics and compute APIs that are heavily used in modern applications.

The Tesla M10’s average benchmark score of 9,724 is actually marginally higher than the K5000’s 9,637, which highlights an important distinction. The M10’s average is buoyed by its two scores, while the K5000’s average is pulled down slightly by its lower Geekbench Metal score of 6,324, a test the M10 does not have. However, when the two cards face each other directly on the same tests, the K5000 wins both. The data shows that the K5000’s advantage is not a fluke of a single test; it consistently outperforms the M10 in both OpenCL and Vulkan workloads. For users prioritizing raw compute throughput in these APIs, the Quadro K5000 is the clear choice based on benchmark results.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA Tesla M10 has a slightly higher average benchmark score of 9,724, compared to the NVIDIA Quadro K5000’s 9,637.

Q: How much faster is the Quadro K5000 in Vulkan performance?

A: The Quadro K5000 scores 11,169 in Geekbench Vulkan, which is 18.3% higher than the Tesla M10’s score of 9,130.

Q: Does the Tesla M10 win any head-to-head benchmark?

A: No. The Quadro K5000 wins both head-to-head benchmarks (Geekbench OpenCL and Geekbench Vulkan) against the Tesla M10.

Q: What is the difference in their FP32 floating-point performance?

A: The Quadro K5000 delivers 2.169 TFLOPS of FP32 performance, while the Tesla M10 provides 1.672 TFLOPS.

Q: Which card has a higher pixel fill rate?

A: The Quadro K5000 has a pixel rate of 22.59 GPixel/s, which is higher than the Tesla M10’s 20.90 GPixel/s.

Q: What is the memory bandwidth for each card?

A: The Quadro K5000 has a memory bandwidth of 172.8 GB/s, while the Tesla M10 has a memory bandwidth of 83.20 GB/s.

Architecture Differences

The two cards are built on different NVIDIA architectures, which explains their divergent performance profiles. The Tesla M10 is based on the GM107 chip using the Maxwell architecture, while the Quadro K5000 uses the GK104 chip with the older Kepler architecture. Despite the generational difference, both are fabricated on the same 28 nm process at TSMC. The transistor counts and die sizes reflect the design philosophies of their respective architectures. The Quadro K5000’s GK104 chip packs 3,540 million transistors on a 294 mm² die, while the Tesla M10’s GM107 has just 1,870 million transistors on a much smaller 148 mm² die. This results in a transistor density of 12.0M / mm² for the K5000 and 12.6M / mm² for the M10, showing that Maxwell is slightly more efficient in packing transistors.

The Kepler architecture in the K5000 is designed with a much wider execution pipeline. It features 1,536 shading units, 128 texture mapping units, and 32 ROPs. In contrast, the Maxwell-based M10 has 640 shading units, 40 TMUs, and 16 ROPs. This massive difference in core counts is the primary reason the K5000 achieves higher raw throughput in the benchmarks, as it has more than double the shading units and four times the TMUs. The M10’s Maxwell architecture compensates with higher clock speeds, running at a base of 1033 MHz and a boost of 1306 MHz, compared to the K5000’s fixed 706 MHz. However, the sheer number of cores in the K5000 still gives it the overall performance edge in FP32 and texture rates. The K5000 also supports a higher Vulkan API version of 1.2.175, while the M10 supports Vulkan 1.4, indicating newer software feature support on the Maxwell card.

Specification Differences

The specification sheets reveal stark contrasts between the two professional GPUs, starting with memory configuration. The Tesla M10 offers 8 GB of GDDR5 memory on a 128-bit bus, yielding a bandwidth of 83.20 GB/s. The Quadro K5000, conversely, has 4 GB of GDDR5 memory on a wider 256-bit bus, which more than doubles the bandwidth to 172.8 GB/s. This makes the K5000 significantly better suited for memory-heavy tasks despite having half the capacity.

Clock speeds and compute rates also differ. The M10 has a base clock of 1033 MHz and a boost clock of 1306 MHz, whereas the K5000 is locked at 706 MHz for both base and boost. In terms of raw compute, the K5000 leads with 2.169 TFLOPS FP32, 90.37 GTexel/s texture rate, and 22.59 GPixel/s pixel rate. The M10 trails with 1.672 TFLOPS FP32, 52.24 GTexel/s, and 20.90 GPixel/s. The K5000’s higher texture and pixel rates are direct results of its larger TMU and ROP counts.

Power and interface specifications also diverge. The Tesla M10 has a much higher TDP of 225 W, requiring a single 8-pin power connector and a suggested 550 W PSU. The Quadro K5000 is far more power-efficient, with a TDP of just 122 W, a single 6-pin connector, and a suggested 300 W PSU. The bus interface differs as well, with the M10 using PCIe 3.0 x16 and the K5000 using the older PCIe 2.0 x16. Display outputs are another major difference: the M10 has no outputs, making it a compute-only card, while the K5000 includes 2x DVI and 2x DisplayPort 1.2 outputs for direct display connectivity. The K5000 also has a listed launch MSRP of 2,499 USD.

The Verdict

The data is unambiguous: pick the Quadro K5000 for any workload that relies on OpenCL or Vulkan performance. It wins both head-to-head tests, with leads of 9.6% and 18.3% respectively. Its higher FP32 throughput (2.169 TFLOPS vs 1.672 TFLOPS), double the memory bandwidth (172.8 GB/s vs 83.20 GB/s), and higher pixel/texture rates make it the stronger compute and graphics performer. The K5000 also offers display outputs, a lower TDP of 122 W versus 225 W, and a simpler power requirement, making it more versatile and easier to integrate into a system.

The Tesla M10 is not without merit, but its strengths lie elsewhere. Its 8 GB of memory, double the K5000’s 4 GB, is a significant advantage for large datasets that need to reside on the GPU. However, the M10’s lower bandwidth and compute rates mean it will process that data more slowly. The M10’s higher average benchmark score of 9,724 versus the K5000’s 9,637 suggests it holds up well in a broader range of tests, but in direct comparisons, it loses. For users who need a compute card with no display output and maximum memory capacity, the M10 is the logical choice. For everyone else, the K5000’s superior performance and lower power draw make it the data-backed winner.

Where Each One Wins

The Quadro K5000 wins in every direct performance comparison, making it the choice for speed-sensitive tasks. Its 18.3% Vulkan advantage and 9.6% OpenCL advantage position it as the better card for GPU-accelerated rendering, scientific computing, and any application that leverages these APIs. The K5000’s higher texture rate (90.37 GTexel/s vs 52.24 GTexel/s) and pixel rate (22.59 GPixel/s vs 20.90 GPixel/s) also make it superior for graphics-intensive workloads like 3D modeling and visualization. Its support for display outputs (2x DVI and 2x DisplayPort 1.2) means it can drive monitors directly, a feature the M10 completely lacks. The K5000 is also the more power-efficient option, with a TDP of 122 W compared to the M10’s 225 W, making it a better fit for workstations with limited power budgets.

The Tesla M10’s wins are less about speed and more about capacity and modernity. Its 8 GB of memory is double the K5000’s 4 GB, making it the preferred card for workloads that require loading very large models or datasets into VRAM, such as deep learning inference or large-scale data analysis. The M10 also has a smaller die (148 mm² vs 294 mm²) and a slightly higher transistor density (12.6M / mm² vs 12.0M / mm²), indicating a more modern design. It supports the newer Vulkan 1.4 API version, while the K5000 is capped at 1.2.175. The M10’s higher boost clock of 1306 MHz (vs 706 MHz) also suggests it can handle bursty workloads that respond well to clock speed. However, its lack of display outputs confines it to server or compute-only roles, and its lower FP32 and bandwidth figures mean it will not match the K5000 in raw computational throughput. The M10 wins for memory capacity and API modernity; the K5000 wins for compute, graphics, and efficiency.