NVIDIA GeForce GTX 960M vs NVIDIA Tesla M10 Comparison

The NVIDIA Tesla M10 and the NVIDIA GeForce GTX 960M are two Maxwell-generation parts that share a surprising amount of silicon, yet target entirely different segments. The M10 is a dual-slot accelerator with no display outputs, built for datacenter workloads, while the GTX 960M is a mobile MXM module designed for laptops. The benchmark data reveals a fascinating split: the GTX 960M leads in OpenCL, while the Tesla M10 counters with a significant Vulkan advantage. This creates a nuanced picture where the "better" card depends entirely on the API and workload in question.

FAQ

Q: Which GPU has a higher average benchmark score?

A: The NVIDIA Tesla M10 has a slightly higher average benchmark score of 9724, compared to the GTX 960M's 9645. This is a marginal difference of less than 1%, placing both cards at the 47th and 46th percentiles of all GPUs, respectively.

Q: How do the two cards compare in Geekbench OpenCL performance?

A: The GTX 960M wins the OpenCL test decisively, scoring 11045 against the Tesla M10's 10318. This represents a 6.6% performance advantage for the mobile GPU in this particular workload.

Q: What about Vulkan performance?

A: The tables turn in the Vulkan benchmark. The Tesla M10 scores 9130, while the GTX 960M manages only 8245. This gives the Tesla M10 a 10.7% lead, making it the clear winner for Vulkan-based applications.

Q: Are the two GPUs based on the same physical chip?

A: Yes, both the Tesla M10 and GTX 960M are built on the GM107 chip. They share the same 28 nm process node, 1,870 million transistors, and a die size of 148 mm², indicating they are fundamentally the same silicon.

Q: What are the key memory specifications for each card?

A: The Tesla M10 comes with 8 GB of GDDR5 memory on a 128-bit bus, delivering 83.20 GB/s of bandwidth. The GTX 960M has 4 GB of GDDR5 memory on the same 128-bit bus, offering slightly less bandwidth at 80.19 GB/s.

Q: Do these cards have the same core configuration?

A: Yes, the compute core counts are identical. Both the Tesla M10 and GTX 960M feature 640 shading units, 40 texture mapping units, and 16 raster operation units.

Where Each One Wins

The data points to a clear split based on the API and workload type. The GTX 960M is the winner in the Geekbench OpenCL test, outperforming the Tesla M10 by 6.6%. This suggests that for general-purpose compute tasks that leverage OpenCL, the mobile GeForce part holds an advantage. The GTX 960M also achieves a higher base clock speed of 1097 MHz, which may contribute to its OpenCL showing.

Conversely, the Tesla M10 dominates in the Geekbench Vulkan test. Its 10.7% lead over the GTX 960M indicates a stronger implementation for Vulkan's lower-level, explicit graphics and compute API. This could be significant for newer game engines or compute frameworks that rely on Vulkan's reduced driver overhead. The Tesla M10 also has double the memory capacity (8 GB vs 4 GB), which is a critical advantage for large datasets or framebuffers that exceed the GTX 960M's capacity.

Architecture Differences

While both cards share the Maxwell architecture and the GM107 chip, their implementations diverge significantly. The Tesla M10 is a dedicated accelerator with a dual-slot form factor, a 225 W TDP, and a single 8-pin power connector. It has no display outputs, confirming its role as a compute-only device for servers or workstations. Its boost clock reaches 1306 MHz, which is higher than the GTX 960M's boost of 1176 MHz.

The GTX 960M is a mobile part using an MXM module form factor. It consumes a much lower 75 W TDP and does not require any external power connectors, as it draws power from the laptop's MXM slot. Its display outputs are "Portable Device Dependent," meaning they are routed through the host laptop. The GTX 960M runs at a higher base clock (1097 MHz vs 1033 MHz) but a lower boost clock (1176 MHz vs 1306 MHz). The Tesla M10's higher boost clock likely explains its Vulkan advantage, while the GTX 960M's higher base clock may help in sustained OpenCL workloads.

Specification Differences

The two cards differ in several key specifications beyond their clock speeds. The most obvious difference is memory capacity: the Tesla M10 offers 8 GB, double the GTX 960M's 4 GB. This directly impacts the maximum dataset size each can handle. Memory bandwidth also differs slightly, with the Tesla M10 at 83.20 GB/s versus 80.19 GB/s for the GTX 960M, despite the GTX 960M's memory running at a slightly lower effective speed of 5 Gbps compared to the M10's 5.2 Gbps.

Power and physical specifications are also starkly different. The Tesla M10 has a 225 W TDP and requires a 550 W power supply, while the GTX 960M's 75 W TDP means it needs no additional power connectors. The Tesla M10 is a 267 mm long, dual-slot card, whereas the GTX 960M is a compact MXM module. The bus interface also differs: the Tesla M10 uses PCIe 3.0 x16, while the GTX 960M uses MXM-B (3.0). Finally, the Tesla M10 has no display outputs, while the GTX 960M's are dependent on the host device.

Head-to-Head Benchmarks

The head-to-head benchmark results show a near-perfect split in wins, with each GPU taking one test. The most decisive victory is the Tesla M10's 10.7% lead in Geekbench Vulkan, where it scored 9130 against the GTX 960M's 8245. This is a substantial margin that suggests the Tesla M10's higher boost clock and driver optimizations provide a significant edge in Vulkan workloads. This result is particularly noteworthy given that the Tesla M10 is not marketed as a gaming card, yet it outperforms a GeForce product by a wide margin in this gaming-relevant API.

The GTX 960M's victory in Geekbench OpenCL is also notable, though the margin is smaller. It scored 11045, which is 6.6% higher than the Tesla M10's 10318. This could be attributed to the GTX 960M's higher base clock, which may allow it to sustain better performance in OpenCL's more CPU-intensive workload patterns. The GTX 960M's average score of 9645 is very close to the Tesla M10's 9724, but its OpenCL result shows that it can punch above its weight in specific scenarios.

Looking at the nearest rivals for each card adds context. The Tesla M10's average score of 9724 places it 0.6% ahead of the Quadro P4000 (9665) and 0.7% ahead of the AMD Radeon Pro WX 2100 (9653). It is also 0.1% behind the Tesla C2070 (9716) and 0.6% behind the GeForce GTX 1070 (9780). The GTX 960M's average score of 9645 is 0.1% behind the Quadro K5000 (9637) and 0.2% behind the Quadro P4000 (9665). This shows that both cards sit in a tightly packed performance bracket, with less than 1% separating them from several professional and consumer alternatives.

The Verdict

The benchmark data suggests that the choice between the Tesla M10 and GTX 960M should be driven by the target API and workload, not overall performance. For Vulkan-based applications, the Tesla M10 is the clear winner with a 10.7% lead. Its 8 GB of memory and higher boost clock make it a more capable accelerator for modern compute and graphics workloads that leverage Vulkan's explicit control. The data indicates that users who prioritize Vulkan performance should select the Tesla M10.

For OpenCL workloads, the GTX 960M is the better option, offering a 6.6% performance advantage. Its higher base clock appears to benefit this API, and its lower power draw makes it a more practical choice for mobile or power-constrained environments. The GTX 960M is also the only one of the two with display outputs, making it suitable for portable systems that need GPU acceleration for both compute and display tasks.

In summary, the Tesla M10 is positioned for datacenter or workstation deployments where Vulkan compute is paramount and memory capacity is critical. The GTX 960M is better suited for laptop users who need strong OpenCL performance and can accept lower memory capacity. The overall benchmark scores are nearly identical, with the Tesla M10's 9724 average only 0.8% higher than the GTX 960M's 9645, but the per-test results reveal that each card has a distinct domain where it excels. The data does not support a single "best" card; instead, it highlights two specialized tools for different jobs.