NVIDIA GeForce MX350 vs NVIDIA Tesla M10 Comparison

Head-to-Head Benchmarks

The recorded data splits this comparison cleanly down the middle. The NVIDIA Tesla M10 wins the OpenCL race by a decisive margin, while the NVIDIA GeForce MX350 takes the Vulkan test with an even larger advantage. Each GPU claims one victory, but the magnitudes of those wins tell a more nuanced story about where each part belongs.

In Geekbench OpenCL, the Tesla M10 posts a score of 10,318, which is 15.8% ahead of the MX350's 8,689. That is not a small gap; it is a substantial lead that places the Tesla M10 in a different performance class for compute-heavy workloads. The MX350, by comparison, sits at 8,689, a figure that puts it roughly in line with the AMD Radeon Pro 450 (10,804, only 0.7% off the MX350's average) and the NVIDIA Quadro K2200 (10,761, 1.1% off). The Tesla M10's OpenCL result is closer to the NVIDIA GeForce GTX 1070 (9,780, within 0.6%) and the NVIDIA Quadro P4000 (9,665, within 0.6%), which suggests the Tesla M10 is operating in a higher tier despite its older architecture.

The Vulkan benchmark flips the script entirely. The MX350 scores 13,077, which is 43.2% ahead of the Tesla M10's 9,130. That is a massive lead, far larger than the Tesla M10's OpenCL advantage. The MX350's Vulkan result is a standout figure, especially when compared to its nearest rivals: the AMD Radeon Pro 450 (10,804), the NVIDIA Quadro K2200 (10,761), the NVIDIA GeForce GTX 1650 SUPER (11,047), and the AMD Radeon RX 550 (11,075). The MX350 outperforms all of these in Vulkan, despite its average benchmark score of 10,883 being only slightly above the field. Meanwhile, the Tesla M10's Vulkan score of 9,130 is its weaker result, and it drags the GPU's average down to 9,724.

Looking at the aggregate averages, the MX350 holds a clear edge. Its average benchmark score is 10,883, compared to the Tesla M10's 9,724. That is a difference of roughly 11.9% in favor of the MX350. The percentile rankings reinforce this: the MX350 sits at the 49th percentile of all GPUs, while the Tesla M10 sits at the 47th. Neither is a top-tier part, but the MX350 is slightly better positioned in the overall distribution.

The delta percentages in the head-to-head data are instructive. When the Tesla M10 wins, it wins by 15.8%. When the MX350 wins, it wins by 43.2%. The MX350's victory is more lopsided, which suggests that in workloads that leverage Vulkan, the MX350 is not just slightly better, it is categorically superior. The Tesla M10's OpenCL win is solid but not overwhelming, and it is offset by a Vulkan performance that is significantly below the MX350's.

One important detail is the benchmark type itself. OpenCL is a general-purpose compute API that is widely used in professional and scientific applications. Vulkan is a low-overhead graphics and compute API that is more common in modern gaming and some professional visualization workloads. The Tesla M10's strength in OpenCL aligns with its intended role as a datacenter virtualization GPU, where compute throughput matters more than graphics-centric API performance. The MX350's Vulkan dominance aligns with its role as a mobile consumer GPU, where modern graphics APIs are the primary driver.

In summary, the head-to-head data shows two GPUs with complementary strengths. The Tesla M10 is the compute specialist, winning OpenCL by 15.8%. The MX350 is the graphics and modern-API specialist, winning Vulkan by 43.2%. The MX350 also has the better average score and the better percentile ranking, which gives it the overall edge in the database's aggregate metrics.

Where Each One Wins

The Tesla M10 wins in scenarios that prioritize raw compute throughput through OpenCL. Its 10,318 OpenCL score is 15.8% above the MX350's 8,689, and that gap is meaningful for tasks like data processing, scientific simulations, or virtualized desktop workloads where OpenCL is the primary execution path. The Tesla M10's average benchmark score of 9,724 places it near the NVIDIA Tesla C2070 (9,716, within 0.1%), the NVIDIA GeForce GTX 1070 (9,780, within 0.6%), and the NVIDIA Quadro P4000 (9,665, within 0.6%). These are all desktop or professional parts with substantially higher power envelopes, which indicates the Tesla M10 is punching at a respectable level for its architecture generation.

The MX350 wins in scenarios that leverage Vulkan. Its 13,077 Vulkan score is 43.2% above the Tesla M10's 9,130, and it also beats all of its nearest rivals in Vulkan: the AMD Radeon Pro 450 (10,804), the NVIDIA Quadro K2200 (10,761), the NVIDIA GeForce GTX 1650 SUPER (11,047), and the AMD Radeon RX 550 (11,075). The MX350's Vulkan result is its standout metric, and it is the primary reason the MX350's average score (10,883) exceeds the Tesla M10's average (9,724).

For gaming or graphics-heavy applications that use Vulkan, the MX350 is clearly the better choice. For compute-heavy applications that use OpenCL, the Tesla M10 is the better choice. The MX350 also has a higher percentile ranking (49th vs. 47th), which indicates it is slightly better positioned relative to the entire GPU landscape.

There is a nuance in the memory configuration that favors the Tesla M10 for certain workloads. The Tesla M10 has 8 GB of GDDR5 memory on a 128-bit bus, yielding 83.20 GB/s of bandwidth. The MX350 has 2 GB of GDDR5 memory on a 64-bit bus, yielding 56.06 GB/s. For workloads that are memory-capacity-bound, such as virtualized environments hosting multiple users, the Tesla M10's larger frame buffer is a decisive advantage. For workloads that are API-performance-bound, the MX350's Vulkan efficiency wins out.

The power situation also matters for deployment. The Tesla M10 has a 225 W TDP, requires a dual-slot cooler, uses a single 8-pin power connector, and needs a 550 W suggested PSU. The MX350 has a 20 W TDP and requires no power connectors, making it suitable for portable devices. The Tesla M10 is a datacenter card with no display outputs; the MX350's display outputs are portable-device-dependent. This means the Tesla M10 is not intended for direct display use, while the MX350 is designed for laptops and small form-factor systems.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The NVIDIA GeForce MX350 has an average benchmark score of 10,883, which is higher than the NVIDIA Tesla M10's average of 9,724.

Q: How large is the Vulkan performance gap between the two?

A: The MX350 scores 13,077 in Geekbench Vulkan, which is 43.2% ahead of the Tesla M10's 9,130.

Q: Which GPU wins in OpenCL performance?

A: The Tesla M10 wins in OpenCL, scoring 10,318 compared to the MX350's 8,689, a lead of 15.8%.

Q: What are the memory specifications of each GPU?

A: The MX350 has 2 GB of GDDR5 memory on a 64-bit bus with 56.06 GB/s bandwidth. The Tesla M10 has 8 GB of GDDR5 memory on a 128-bit bus with 83.20 GB/s bandwidth.

Q: How do their power requirements differ?

A: The MX350 has a 20 W TDP and requires no power connectors. The Tesla M10 has a 225 W TDP, uses a dual-slot cooler, requires a single 8-pin power connector, and needs a suggested 550 W PSU.

Q: Which GPU is better positioned relative to all other GPUs?

A: The MX350 is at the 49th percentile of all GPUs, while the Tesla M10 is at the 47th percentile. The MX350 is slightly better positioned.

Specification Differences

The two GPUs differ across nearly every major specification. The MX350 uses a GP107S chip built on a 14 nm process at Samsung, while the Tesla M10 uses a GM107 chip built on a 28 nm process at TSMC. The MX350 has 3,300 million transistors on a 132 mm² die, giving a transistor density of 25.0 million per mm². The Tesla M10 has 1,870 million transistors on a 148 mm² die, giving a transistor density of 12.6 million per mm². The MX350 is the denser chip by a wide margin.

Clock speeds differ as well. The MX350 has a base clock of 1354 MHz and a boost clock of 1468 MHz. The Tesla M10 has a base clock of 1033 MHz and a boost clock of 1306 MHz. The MX350 runs at higher frequencies in both states.

Memory is another major divider. The MX350 has 2 GB of GDDR5 on a 64-bit bus with 56.06 GB/s bandwidth and a memory clock of 1752 MHz (7 Gbps effective). The Tesla M10 has 8 GB of GDDR5 on a 128-bit bus with 83.20 GB/s bandwidth and a memory clock of 1300 MHz (5.2 Gbps effective). The Tesla M10 has four times the capacity and roughly 48% more bandwidth, despite the lower memory clock.

The compute units differ in configuration. Both have 640 shading units, but the MX350 has 32 TMUs and 16 ROPs, while the Tesla M10 has 40 TMUs and 16 ROPs. Pixel rates are close: the MX350 produces 23.49 GPixel/s, and the Tesla M10 produces 20.90 GPixel/s. Texture rates favor the Tesla M10: 52.24 GTexel/s versus 46.98 GTexel/s. FP32 performance favors the MX350: 1.879 TFLOPS versus 1.672 TFLOPS. The MX350 also supports FP16 at 29.36 GFLOPS (1:64 ratio), while the Tesla M10 has no recorded FP16 capability.

Power and physical requirements differ sharply. The MX350 has a 20 W TDP and no power connectors. The Tesla M10 has a 225 W TDP, is dual-slot, requires a single 8-pin power connector, and has a suggested 550 W PSU. The Tesla M10 is 267 mm long (10.5 inches); the MX350 has no recorded dimensions, as it is designed for portable devices. The bus interface also differs: the MX350 uses PCIe 3.0 x4, while the Tesla M10 uses PCIe 3.0 x16. Display outputs are portable-device-dependent for the MX350, while the Tesla M10 has no outputs.

DirectX support differs: the MX350 supports DirectX 12 (12_1), while the Tesla M10 supports DirectX 12 (11_0). Both support OpenGL 4.6 and Vulkan 1.4.

Architecture Differences

The MX350 is built on NVIDIA's Pascal architecture, while the Tesla M10 uses the older Maxwell architecture. This architectural generation gap explains several performance differences. Pascal brings improved scheduling, better memory compression, and more efficient geometry processing compared to Maxwell. The MX350's higher FP32 throughput (1.879 TFLOPS vs. 1.672 TFLOPS) and higher pixel rate (23.49 GPixel/s vs. 20.90 GPixel/s) reflect these architectural improvements, even though the Tesla M10 has more TMUs (40 vs. 32) and a higher texture rate (52.24 GTexel/s vs. 46.98 GTexel/s).

The process node difference is significant. The MX350 is fabricated on a 14 nm process at Samsung, while the Tesla M10 is fabricated on a 28 nm process at TSMC. The 14 nm node allows the MX350 to pack 25.0 million transistors per mm², compared to the Tesla M10's 12.6 million per mm². The MX350 achieves 3,300 million transistors on a smaller die (132 mm²), while the Tesla M10 uses 1,870 million transistors on a larger die (148 mm²). This density advantage translates directly into the MX350's dramatically lower power draw: 20 W versus 225 W.

The memory architecture also differs at a fundamental level. The MX350 uses a 64-bit memory bus, while the Tesla M10 uses a 128-bit bus. The Tesla M10's wider bus provides 83.20 GB/s of bandwidth, compared to the MX350's 56.06 GB/s, and the Tesla M10 also has 8 GB of memory versus the MX350's 2 GB. For workloads that need large memory footprints, the Tesla M10 is the only viable option between the two.

The FP16 situation is notable. The MX350 supports FP16 at 29.36 GFLOPS with a 1:64 ratio, meaning FP16 throughput is only a fraction of FP32 throughput. The Tesla M10 has no recorded FP16 support. This is not a meaningful advantage for the MX350 in practice, since the 1:64 ratio means FP16 performance is severely limited, but it does indicate a more modern feature set.

The Tesla M10 has a clear lineage: its predecessor is Tesla Kepler and its successor is Tesla Pascal. The MX350 belongs to the GeForce MX (3xx) generation with no recorded predecessor or successor. The Tesla M10's generation is Tesla Maxwell (Mxx), while the MX350's generation is GeForce MX (3xx).

Both GPUs are end-of-life, but their release dates differ. The MX350 was released on February 9, 2020, while the Tesla M10 was released on May 17, 2016. The Tesla M10 is nearly four years older, which explains its older Maxwell architecture and 28 nm process.

The Tesla M10 is a dual-slot card with no display outputs, designed for server environments. The MX350 is a portable-device GPU with display outputs that depend on the host device. These are fundamentally different product categories: one is a datacenter compute accelerator, and the other is a mobile consumer GPU.

The Verdict

The data points to a straightforward conclusion: choose the NVIDIA GeForce MX350 for modern graphics workloads and overall aggregate performance, and choose the NVIDIA Tesla M10 for OpenCL-heavy compute tasks and memory-capacity-bound workloads.

The MX350 wins the average benchmark battle with a score of 10,883 versus 9,724, and it holds the higher percentile ranking at 49th versus 47th. Its Vulkan score of 13,077 is a standout, beating the Tesla M10 by 43.2% and also exceeding all of its nearest rivals in that test. For anyone running Vulkan-based applications, gaming, or modern graphics APIs, the MX350 is the clear pick. Its higher clocks (1354 MHz base, 1468 MHz boost), higher FP32 throughput (1.879 TFLOPS), and higher pixel rate (23.49 GPixel/s) all support this conclusion. Its 20 W TDP makes it suitable for portable devices with no power connector requirements.

The Tesla M10 wins OpenCL with a score of 10,318, which is 15.8% ahead of the MX350. Its 8 GB memory capacity and 83.20 GB/s bandwidth are major advantages for workloads that require large frame buffers or high memory throughput. Its 128-bit bus and 40 TMUs give it a higher texture rate (52.24 GTexel/s). For datacenter deployments, virtualized environments, or scientific computing that relies on OpenCL, the Tesla M10 is the better choice. It is a dual-slot card with a 225 W TDP and requires a single 8-pin power connector and a suggested 550 W PSU, so it is not a drop-in for portable systems.

There is no universal winner. The MX350 is the better overall GPU according to the database's aggregate metrics, but the Tesla M10 has specific strengths that matter in professional compute contexts. The decision should be driven by the workload: Vulkan and general graphics point to the MX350; OpenCL and memory-intensive compute point to the Tesla M10.