NVIDIA Quadro 6000 vs NVIDIA Tesla M10 Comparison

The NVIDIA Quadro 6000 and NVIDIA Tesla M10 represent two very different eras of GPU design, and the benchmark data reflects that shift. In the single available head-to-head test, the Tesla M10 takes the win, but the broader context of their specifications tells a more nuanced story about which card suits which workload. The Quadro 6000 is a Fermi-era professional workstation card from 2010, while the Tesla M10 is a Maxwell-based accelerator from 2016, and their architectural differences are stark.

Head-to-Head Benchmarks

The only direct comparison available is the Geekbench OpenCL test. Here, the Tesla M10 scores 10,318 points, while the Quadro 6000 scores 9,846 points. That is a delta of -4.6% from the perspective of the Quadro, meaning the Tesla M10 is about 4.6% faster in this particular compute workload. While the percentage gap is modest, it is consistent with the Tesla's architectural advantages in raw parallel throughput.

Looking at the rivals surrounding each card, the picture becomes clearer. The Quadro 6000’s nearest rival is the NVIDIA Quadro M2000M, which scores 9,832 points — just 0.1% behind the Quadro 6000. That is an essentially negligible difference, putting the Quadro 6000 in the same performance class as a much newer mobile workstation GPU. The AMD FirePro W5000 sits 0.4% behind at 9,803 points, and the NVIDIA GeForce GTX 1070 trails by 0.7% at 9,780 points. On the other side, the Quadro 6000 is 1.1% ahead of the NVIDIA GeForce GTX 870M, which scores 9,959 points.

The Tesla M10’s nearest rivals tell a similar story of tight clustering. The Tesla C2070 scores 9,716 points, just 0.1% behind the M10. The GeForce GTX 1070 is 0.6% behind at 9,780 points, while the Quadro P4000 trails by 0.6% at 9,665 points and the AMD Radeon Pro WX 2100 is 0.7% behind at 9,653 points. Notably, both cards hold a percentile rank of 47 against all GPUs, meaning they land in essentially the same position in the overall performance distribution.

The Tesla M10 also has a Vulkan score of 9,130 points, a test the Quadro 6000 does not have data for. This suggests the Maxwell architecture has better support for modern graphics APIs, though the Quadro's absence of a Vulkan result in the data limits direct comparison. What the data shows is that in OpenCL, the Tesla M10 is the clear winner, but only by a narrow margin that would be imperceptible in most real-world tasks.

The Verdict

From the data, the Tesla M10 is the better choice for anyone prioritizing raw OpenCL compute performance. It wins the only head-to-head benchmark available, and its 1.672 TFLOPS of FP32 throughput is substantially higher than the Quadro 6000’s 1,027.7 GFLOPS. The M10 also offers more memory at 8 GB versus 6 GB, and it supports PCIe 3.0 x16, which provides double the bandwidth of the Quadro's PCIe 2.0 x16 interface. If your workload is compute-bound and you need the latest interface standards, the Tesla M10 is the data-backed pick.

The Quadro 6000, however, is not without its reasons to exist. It has a much higher memory bandwidth at 143.4 GB/s compared to the Tesla M10’s 83.20 GB/s, which can matter for memory-heavy tasks that are less reliant on raw FP32 compute. It also includes display outputs — 1x DVI, 2x DisplayPort, and 1x S-Video — while the Tesla M10 has no outputs at all. If you need a card that can drive monitors and perform compute duties, the Quadro 6000 is the only option of the two. The Quadro 6000 also has a launch MSRP of 4,399 USD, though the Tesla M10 has no listed launch MSRP in the data.

For a server or dedicated compute node where display output is irrelevant, the Tesla M10 is the superior choice. For a workstation that must double as a display adapter, the Quadro 6000’s output capabilities and higher memory bandwidth make it a defensible, if older, selection. The data does not support calling the Quadro 6000 a faster card overall — it simply offers a different set of trade-offs.

FAQ

Q: Which card is faster in OpenCL?

A: The Tesla M10 scores 10,318 points versus the Quadro 6000’s 9,846 points, making it 4.6% faster in the Geekbench OpenCL test.

Q: Does the Quadro 6000 support display output?

A: Yes, it has 1x DVI, 2x DisplayPort, and 1x S-Video outputs. The Tesla M10 has no display outputs.

Q: Which card has more memory bandwidth?

A: The Quadro 6000 has 143.4 GB/s of bandwidth, while the Tesla M10 has 83.20 GB/s. This is a significant advantage for the Quadro in memory-bound tasks.

Q: Which card has a higher FP32 compute rating?

A: The Tesla M10 is rated at 1.672 TFLOPS, while the Quadro 6000 is rated at 1,027.7 GFLOPS. The Tesla has roughly 63% more FP32 throughput.

Q: Do both cards support the same PCIe generation?

A: No. The Tesla M10 uses PCIe 3.0 x16, while the Quadro 6000 uses PCIe 2.0 x16. This gives the Tesla a potential bandwidth advantage for data transfer.

Q: What are the nearest rivals for each card?

A: The Quadro 6000’s closest rival is the Quadro M2000M (0.1% behind), and the Tesla M10’s closest rival is the Tesla C2070 (0.1% behind). Both cards also sit near the GeForce GTX 1070 in performance, within 0.7% for the Quadro and 0.6% for the Tesla.

Specification Differences

The two cards diverge sharply on memory configuration. The Quadro 6000 has 6 GB of GDDR5 on a 384-bit bus, yielding 143.4 GB/s of bandwidth. The Tesla M10 has 8 GB of GDDR5 on a 128-bit bus, yielding 83.20 GB/s. That is a 2 GB capacity advantage for the Tesla, but a 60.2 GB/s bandwidth advantage for the Quadro.

Clock speeds also differ. The Quadro 6000 lists only a memory clock of 747 MHz (3 Gbps effective), with no base or boost clock provided. The Tesla M10 has a base clock of 1033 MHz and a boost clock of 1306 MHz, with memory at 1300 MHz (5.2 Gbps effective). The Tesla’s higher core clocks contribute to its FP32 advantage.

The compute units differ in composition. The Quadro 6000 has 448 shading units, 56 TMUs, and 48 ROPs. The Tesla M10 has 640 shading units, 40 TMUs, and 16 ROPs. The Tesla has 192 more shading units, but the Quadro has 16 more TMUs and 32 more ROPs. This explains why the Quadro’s pixel rate of 16.07 GPixel/s is lower than the Tesla’s 20.90 GPixel/s, despite the Quadro’s higher memory bandwidth.

Power and physical specs differ as well. The Quadro 6000 has a TDP of 204 W and requires 1x 6-pin + 1x 8-pin power connectors. The Tesla M10 has a TDP of 225 W and requires a single 8-pin connector. Both are dual-slot cards and both suggest a 550 W power supply. The Quadro is 248 mm long (9.8 inches), while the Tesla is 267 mm long (10.5 inches).

Architecture Differences

The Quadro 6000 is built on the GF100 chip using the Fermi architecture, manufactured on a 40 nm process at TSMC. It packs 3,100 million transistors on a 529 mm² die, giving a transistor density of 5.9M per mm². The Tesla M10 uses the GM107 chip on the Maxwell architecture, also from TSMC but on a 28 nm process. It has 1,870 million transistors on a much smaller 148 mm² die, yielding a higher density of 12.6M per mm².

The generation names reflect their positioning: the Quadro is from the "Quadro Fermi (x000)" generation, while the Tesla is from the "Tesla Maxwell (Mxx)" generation. The Quadro’s predecessor is the Quadro FX Tesla, and its successor is the Quadro Kepler. The Tesla’s predecessor is the Tesla Kepler, and its successor is the Tesla Pascal.

API support is another differentiator. Both cards support DirectX 12 (11_0) and OpenGL 4.6. However, the Tesla M10 adds Vulkan 1.4 support, while the Quadro 6000 has no Vulkan data listed. This makes the Tesla more future-proof for modern graphics workloads that leverage Vulkan.

The Tesla M10 also benefits from a newer bus interface — PCIe 3.0 x16 versus PCIe 2.0 x16 on the Quadro. This is a meaningful architectural upgrade that affects data transfer speeds with the host system, even if it does not directly improve compute throughput.

Where Each One Wins

The Tesla M10 wins in raw compute throughput. Its FP32 rating of 1.672 TFLOPS is significantly higher than the Quadro 6000’s 1,027.7 GFLOPS, and its OpenCL score of 10,318 beats the Quadro’s 9,846. It also wins on memory capacity (8 GB versus 6 GB), core clock speed (1033 MHz base / 1306 MHz boost versus no listed base or boost), and interface generation (PCIe 3.0 versus 2.0). For compute-heavy workloads like machine learning inference, scientific simulation, or any task that scales with shading units, the Tesla M10 is the clear winner.

The Quadro 6000 wins on memory bandwidth, with 143.4 GB/s versus the Tesla’s 83.20 GB/s. It also wins on display connectivity, with three output types (DVI, DisplayPort, S-Video) versus none on the Tesla. For tasks that are bandwidth-bound — such as large dataset transfers, texture-heavy rendering, or multi-display visualization — the Quadro’s wider 384-bit memory bus gives it a distinct edge. Its higher ROP count (48 versus 16) also suggests better performance in fill-rate-limited scenarios, though the Tesla’s higher pixel rate (20.90 GPixel/s versus 16.07 GPixel/s) complicates that picture.

In practical terms, the Tesla M10 is built for headless compute acceleration in servers. The Quadro 6000 is a workstation card that can serve double duty as a display adapter and compute device. If you need a card to sit in a server room and crunch numbers, the Tesla M10 is the data-supported choice. If you need a card for a desktop workstation that must output to monitors, the Quadro 6000’s display outputs and superior memory bandwidth make it the only viable option of the two, despite its lower compute scores.