NVIDIA GeForce GTX 950A vs NVIDIA Tesla M10 Comparison

The NVIDIA GeForce GTX 950A and NVIDIA Tesla M10 are both built on the same GM107 Maxwell chip, yet they serve entirely different purposes in the hardware ecosystem. The GTX 950A is a mobile-oriented MXM module from the GeForce 900A generation, while the Tesla M10 is a dual-slot, server-grade accelerator with no display outputs. Benchmark data shows they are remarkably close in raw compute performance, but their memory subsystems, power profiles, and intended roles create clear separation.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, where the Tesla M10 edges out the GTX 950A by a razor-thin margin. The Tesla M10 scores 10,318, while the GTX 950A scores 10,273, resulting in a delta of -0.4% for the latter. This is a negligible difference in raw compute throughput—effectively a statistical tie in OpenCL workloads. Both cards are powered by the same GM107 chip with 640 shading units, 40 texture mapping units, and 16 raster operation units, so the near-identical scores make architectural sense.

However, the Tesla M10’s higher clock speeds tell a more nuanced story. The Tesla M10 runs at a base clock of 1033 MHz and boosts up to 1306 MHz, whereas the GTX 950A operates at 993 MHz base and 1124 MHz boost. That translates to a pixel rate of 20.90 GPixel/s and a texture rate of 52.24 GTexel/s for the Tesla M10, compared to 17.98 GPixel/s and 44.96 GTexel/s for the GTX 950A. In FP32 compute, the Tesla M10 delivers 1.672 TFLOPS against the GTX 950A’s 1,438.7 GFLOPS—roughly a 16% theoretical advantage for the Tesla part. Yet the OpenCL benchmark shows only a 0.4% gap, suggesting that memory bandwidth or driver overhead is limiting the Tesla M10 in this particular test.

The Tesla M10 also has a second benchmark result: a Geekbench Vulkan score of 9,130. The GTX 950A has no Vulkan score listed, so a direct comparison is impossible. But the Vulkan score is notably lower than the OpenCL score for the same card, indicating that the Tesla M10 is optimized for compute APIs rather than graphics-oriented workloads. The GTX 950A’s nearest rivals include the AMD Radeon RX 6500M (10,362, delta -0.9%), the NVIDIA Tesla C2075 (10,400, delta -1.2%), and the AMD Radeon R9 M375 (10,070, delta +2%). Meanwhile, the Tesla M10 sits close to the NVIDIA Tesla C2070 (9,716, delta +0.1%), the NVIDIA GeForce GTX 1070 (9,780, delta -0.6%), and the NVIDIA Quadro P4000 (9,665, delta +0.6%). These rival clusters show both cards are firmly mid-pack performers, with the GTX 950A ranking at the 48th percentile of all GPUs and the Tesla M10 at the 47th.

The Verdict

The data makes one thing clear: if you need raw OpenCL compute in a compact mobile form factor, the GTX 950A is the pragmatic choice. It delivers 10,273 in OpenCL, just 45 points behind the Tesla M10, while consuming only 75 W and requiring no power connectors. The Tesla M10, by contrast, draws 225 W, needs a 1x 8-pin power connector, and demands a 550 W suggested PSU. For a performance difference of 0.4%, the power and thermal penalty of the Tesla M10 is enormous.

However, the Tesla M10 wins decisively on memory. It packs 8 GB of GDDR5 on a 128-bit bus, yielding 83.20 GB/s of bandwidth, compared to the GTX 950A’s 2 GB of DDR3 at 32.03 GB/s. That is a 2.6x bandwidth advantage and 4x capacity advantage for the Tesla M10. For workloads that are memory-bound—such as large dataset processing or virtual desktop infrastructure—the Tesla M10’s memory subsystem is the clear winner despite the similar compute scores.

Choose the GTX 950A if you need an end-of-life mobile GPU for a laptop or embedded system where power efficiency matters and 2 GB of memory is sufficient. Choose the Tesla M10 if you are building a server or workstation with PCIe 3.0 x16, need 8 GB of GDDR5 capacity, and can tolerate a 225 W dual-slot card with no display outputs. The Vulkan score of 9,130 on the Tesla M10 also suggests it has some graphics capability, but its primary role is compute acceleration.

Architecture Differences

Both cards share the same fundamental architecture: the Maxwell design from NVIDIA, fabricated on a 28 nm process at TSMC. The chip is GM107, with 1,870 million transistors packed into a 148 mm² die, yielding a transistor density of 12.6M per mm². This is identical silicon, so the core compute resources are the same: 640 shading units, 40 TMUs, and 16 ROPs. Neither card has RT cores or tensor cores, as both predate the ray tracing and AI accelerator era.

The differences emerge in implementation. The GTX 950A uses DDR3 memory with a 1001 MHz clock (2 Gbps effective), while the Tesla M10 uses GDDR5 at 1300 MHz (5.2 Gbps effective). The memory bus width is the same at 128 bits, but the GDDR5’s higher data rate produces 83.20 GB/s versus 32.03 GB/s—a massive bandwidth gap. The Tesla M10 also runs at higher clocks: base 1033 MHz versus 993 MHz, and boost 1306 MHz versus 1124 MHz.

The physical form factors diverge sharply. The GTX 950A is an MXM Module with an MXM-B (3.0) bus interface and portable-device-dependent display outputs. The Tesla M10 is a dual-slot card measuring 267 mm (10.5 inches) in length, using PCIe 3.0 x16, with no display outputs whatsoever. Power delivery also differs: the GTX 950A draws 75 W with no power connectors, while the Tesla M10 draws 225 W via a single 8-pin connector and requires a 550 W suggested PSU.

Both cards support DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.4, so API compatibility is identical. The GTX 950A was released in March 2015, while the Tesla M10 came later in May 2016. The GTX 950A’s predecessor is the GeForce 800A, and the Tesla M10’s predecessor is Tesla Kepler with Tesla Pascal as its successor. Both are end-of-life products.

FAQ

Q: Which card has better raw compute performance in OpenCL?

A: The Tesla M10 scores 10,318 in Geekbench OpenCL, which is 0.4% higher than the GTX 950A’s 10,273. This is a negligible difference, effectively making them equal in OpenCL compute tasks.

Q: How do the memory configurations compare?

A: The GTX 950A has 2 GB of DDR3 on a 128-bit bus with 32.03 GB/s bandwidth. The Tesla M10 has 8 GB of GDDR5 on the same 128-bit bus but with 83.20 GB/s bandwidth—2.6x the bandwidth and 4x the capacity.

Q: What is the power consumption difference?

A: The GTX 950A draws 75 W with no power connectors, while the Tesla M10 draws 225 W via a single 8-pin connector and has a suggested PSU of 550 W.

Q: Can the Tesla M10 output video?

A: No. The Tesla M10 has no display outputs, making it unsuitable for direct monitor connection. The GTX 950A’s display outputs are portable-device-dependent, meaning they vary by the host system.

Q: Which card has higher clock speeds?

A: The Tesla M10 runs at 1033 MHz base and 1306 MHz boost, compared to the GTX 950A’s 993 MHz base and 1124 MHz boost. The Tesla M10 also has higher memory clocks at 1300 MHz versus 1001 MHz.

Q: Do they support the same graphics APIs?

A: Yes. Both cards support DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.4, so software compatibility is identical across the two.

Where Each One Wins

The GTX 950A wins in power efficiency and physical flexibility. At 75 W with no external power connectors, it can slot into MXM-based laptops and compact systems where the Tesla M10’s 225 W draw and dual-slot footprint are impossible. Its portable-device-dependent display outputs mean it can drive a screen in the right host, whereas the Tesla M10 is headless by design. In the OpenCL benchmark, the GTX 950A trails by only 0.4%, so it loses almost nothing in compute despite using far less power.

The Tesla M10 wins on memory bandwidth, capacity, and raw theoretical throughput. Its 83.20 GB/s bandwidth is a 2.6x advantage over the GTX 950A, and the 8 GB GDDR5 frame buffer is four times larger. The higher clock speeds also yield a 20.90 GPixel/s pixel rate and 52.24 GTexel/s texture rate, versus 17.98 GPixel/s and 44.96 GTexel/s for the GTX 950A. FP32 compute is similarly higher at 1.672 TFLOPS versus 1,438.7 GFLOPS—a 16% theoretical lead that the OpenCL benchmark does not fully reflect due to real-world constraints.

The Tesla M10 also wins on server integration. It uses PCIe 3.0 x16, the standard for rack-mounted accelerators, while the GTX 950A’s MXM-B (3.0) interface is laptop-centric. The Tesla M10’s Vulkan score of 9,130, while lower than its OpenCL result, still demonstrates compute capability across multiple API families. In a datacenter context, the Tesla M10’s 8 GB memory and headless design are advantages for virtualized workloads, even though its power draw is substantial. For a mobile workstation or embedded system, the GTX 950A is the only viable option due to its form factor, and its OpenCL score proves it is not a performance sacrifice.