NVIDIA Tesla C2070 vs NVIDIA Tesla M10 Comparison

The NVIDIA Tesla M10 and NVIDIA Tesla C2070 represent two very different generations of NVIDIA's compute-focused hardware, separated by five years of architectural evolution. The data shows a fascinating contest where the older, larger Fermi chip holds its ground in aggregate scores, yet the newer Maxwell part pulls ahead in the key raw compute test. The M10 wins the only head-to-head benchmark, but the C2070 remains remarkably competitive in overall averages.

Head-to-Head Benchmarks

The sole direct comparison available is the Geekbench OpenCL test, and it clearly favors the newer card. The NVIDIA Tesla M10 scores 10318 points, while the NVIDIA Tesla C2070 scores 9716 points. This translates to a 6.2% victory for the M10. This is a significant margin in compute accelerators, suggesting that the architectural efficiency of Maxwell provides a tangible advantage in this particular workload.

Looking at the broader picture, the average benchmark scores tell a slightly different story. The M10's average is 9724, while the C2070's average is 9716. The difference is a mere 0.1%, which is essentially a statistical tie. This is remarkable given the generational gap. The data implies that while the M10 can win individual tests, the C2070's raw capabilities allow it to remain competitive in the aggregate, preventing a landslide victory for the newer product.

The performance context is further defined by their shared 47th percentile ranking among all GPUs. This places both cards in the same performance tier, despite their different architectures and release dates. The nearest rival data reinforces this image: the GeForce GTX 1070 averages 9780 (a -0.6% delta vs M10, -0.7% vs C2070), and the Quadro P4000 averages 9665 (0.6% and 0.5% deltas respectively). Both Tesla cards are bracketed by these mainstream and professional parts, indicating they occupy a similar performance class for compute tasks.

Architecture Differences

The most profound differences lie in the fundamental design of each chip. The Tesla M10 is built on the Maxwell architecture using the GM107 chip, manufactured on a 28 nm process at TSMC. In contrast, the Tesla C2070 uses the Fermi architecture with the GF100 chip, built on a much older 40 nm process, also at TSMC. This process shrink is a primary driver of the M10's efficiency.

The physical characteristics of the chips are starkly different. The C2070's GF100 die is massive at 529 mm², housing 3,100 million transistors. The M10's GM107 die is far smaller at 148 mm², with 1,870 million transistors. This results in a transistor density of 12.6M / mm² for the M10 versus 5.9M / mm² for the C2070. The newer part packs more transistors into a smaller space, which confirms the manufacturing improvements.

Memory configurations also diverge significantly. The M10 features 8 GB of GDDR5 memory on a 128-bit bus, yielding a bandwidth of 83.20 GB/s. The C2070, despite having less total memory at 6 GB, uses a much wider 384-bit bus, resulting in a substantially higher bandwidth of 143.4 GB/s. This is a critical distinction, as memory bandwidth is often a bottleneck for compute workloads.

The compute core counts are a study in compromise. The M10 has 640 shading units, 40 texture mapping units (TMUs), and 16 raster output units (ROPs). The C2070 has fewer shading units at 448, but more TMUs at 56 and significantly more ROPs at 48. This suggests the C2070 was designed with a different balance of compute and rasterization capabilities.

Clock speeds and power draw tell the efficiency story. The M10 has a base clock of 1033 MHz and a boost clock of 1306 MHz, while consuming 225 W. The C2070 has no listed base or boost clock in the data, but its memory runs at 747 MHz, and it consumes a slightly higher 238 W. The M10 delivers higher performance with lower power draw, a direct benefit of the newer process node.

The Verdict

The data presents a nuanced picture for potential users. The Tesla M10 is the clear winner in raw compute performance as measured by the Geekbench OpenCL test, posting a 6.2% advantage. Its higher FP32 throughput of 1.672 TFLOPS versus the C2070's 1,027.7 GFLOPS reinforces this. For workloads that are heavily dependent on floating-point math, the M10 is the superior choice.

However, the Tesla C2070 cannot be dismissed. Its average benchmark score is virtually identical to the M10, with only a 0.1% difference. The C2070's massive memory bandwidth (143.4 GB/s versus 83.20 GB/s) could be a decisive factor for applications that are memory-bound rather than compute-bound. This makes it a viable option for specific tasks where moving data quickly is more important than raw FLOPs.

The choice is not about which is objectively better, but which is better for the specific workload. The M10's win in the head-to-head test and its higher compute density make it the safer bet for general compute acceleration. The C2070's unique memory advantage makes it a specialist for bandwidth-intensive tasks. Given the M10's victory in the direct comparison and its more modern feature set, the data slightly favors it as the more versatile accelerator.

Specification Differences

• Architecture: The M10 uses Maxwell, while the C2070 uses Fermi.
• Process Node: The M10 is built on 28 nm, the C2070 on 40 nm.
• Die Size: The M10's die is 148 mm², compared to the C2070's 529 mm².
• Transistors: The M10 has 1,870 million, the C2070 has 3,100 million.
• Memory Size: The M10 has 8 GB, the C2070 has 6 GB.
• Memory Bus Width: The M10 uses a 128-bit bus, the C2070 a 384-bit bus.
• Memory Bandwidth: The M10 offers 83.20 GB/s, the C2070 offers 143.4 GB/s.
• Shading Units: The M10 has 640, the C2070 has 448.
• Texture Mapping Units: The M10 has 40, the C2070 has 56.
• Raster Output Units: The M10 has 16, the C2070 has 48.
• FP32 Performance: The M10 is rated at 1.672 TFLOPS, the C2070 at 1,027.7 GFLOPS.
• Power Connectors: The M10 requires 1x 8-pin, the C2070 needs 1x 6-pin + 1x 8-pin.
• Bus Interface: The M10 uses PCIe 3.0 x16, the C2070 uses PCIe 2.0 x16.
• Display Outputs: The M10 has No outputs, the C2070 has 1x DVI.
• Vulkan Support: The M10 supports Vulkan 1.4, the C2070 has null support.

FAQ

Q: Which card performs better in Geekbench OpenCL?

A: The NVIDIA Tesla M10 scores 10318 compared to the C2070's 9716, giving the M10 a 6.2% lead in this test.

Q: How do their average benchmark scores compare?

A: The M10 has an average score of 9724, while the C2070 averages 9716. The difference is only 0.1%, making them statistically tied in aggregate.

Q: Which card has more memory bandwidth?

A: The Tesla C2070 has significantly higher memory bandwidth at 143.4 GB/s, compared to the M10's 83.20 GB/s.

Q: What are the key architectural differences?

A: The M10 is based on the Maxwell architecture on a 28 nm process, while the C2070 is based on the older Fermi architecture on a 40 nm process.

Q: Which card has a higher FP32 compute rating?

A: The Tesla M10 is rated for 1.672 TFLOPS, which is considerably higher than the C2070's 1,027.7 GFLOPS.

Q: Do both cards support the same APIs?

A: Both support DirectX 12 (11_0) and OpenGL 4.6, but the M10 supports Vulkan 1.4 while the C2070 has no listed Vulkan support.

Where Each One Wins

The NVIDIA Tesla M10 is the winner for tasks that prioritize raw floating-point compute throughput. Its 6.2% advantage in the Geekbench OpenCL test, combined with its higher FP32 rating of 1.672 TFLOPS, makes it the clear choice for general-purpose compute acceleration, machine learning inference, and other workloads that stress the shader cores. Its smaller die size and lower power draw of 225 W also make it a more practical option for dense server deployments where space and cooling are at a premium.

The NVIDIA Tesla C2070 wins in scenarios where memory bandwidth is the limiting factor. Its 143.4 GB/s bandwidth, nearly double that of the M10, is a massive advantage for applications like large dataset processing, scientific simulations with high memory traffic, and certain types of data analytics. The wider 384-bit memory bus allows it to feed its compute cores far more effectively in memory-bound situations. Its older PCIe 2.0 x16 interface may be a bottleneck, but within its own board, the data flow is superior. For a specific workload that is starved for data, the C2070's architecture is purpose-built to deliver.