NVIDIA GeForce MX350 vs NVIDIA Tesla C2075 Comparison

Head-to-Head Benchmarks

The single direct comparison available between these two GPUs is the Geekbench OpenCL test, and the result is decisive. The NVIDIA Tesla C2075 scores 10,400, while the NVIDIA GeForce MX350 manages only 8,689. That represents a 16.5% advantage for the Tesla C2075, making it the clear winner in the only head-to-head benchmark recorded. The MX350 does not win a single benchmark in this matchup; the tally stands at zero wins for the GeForce MX350 and one win for the Tesla C2075.

Diving into the broader benchmark context, the average benchmark score for the MX350 is 10,883, which is notably higher than its OpenCL score alone. This is driven by its strong Vulkan performance of 13,077, a test the Tesla C2075 does not appear in. The Tesla C2075's average benchmark score is 10,400, identical to its OpenCL result, indicating it has no other recorded benchmark data to pull its average up or down.

When placed against their respective nearest rivals, the two cards occupy similar competitive territory. The MX350 sits at the 49th percentile of all GPUs, with rivals that are tightly clustered. Its average score of 10,883 is a mere 0.7% ahead of the AMD Radeon Pro 450 (10,804) and 1.1% ahead of the NVIDIA Quadro K2200 (10,761). Conversely, it trails the NVIDIA GeForce GTX 1650 SUPER (11,047) by 1.5% and the AMD Radeon RX 550 (11,075) by 1.7%. The Tesla C2075, at the 48th percentile, shows a similar pattern: it is 0.4% ahead of the AMD Radeon RX 6500M (10,362) and 1.2% ahead of the NVIDIA GeForce GTX 950A (10,273), but falls behind the AMD Radeon RX 550X (10,481) by 0.8% and the AMD Radeon R9 M275X (10,582) by 1.7%.

The deltaPct figures tell a story of two GPUs that are both competitive within their own performance tier. The MX350's 16.5% deficit in OpenCL is significant, but its Vulkan score suggests it has strengths in other API workloads. The Tesla C2075's 16.5% lead in OpenCL is its headline achievement, yet it lacks the Vulkan data that could reveal a different picture.

The Verdict

Based strictly on the benchmark data, the NVIDIA Tesla C2075 is the superior performer in the OpenCL workload, holding a 16.5% lead over the GeForce MX350. For applications that rely on OpenCL compute, the choice is unambiguous. The Tesla C2075's score of 10,400 versus the MX350's 8,689 is a substantial margin that would translate into measurable performance differences in real-world compute tasks.

However, the GeForce MX350 is not without its own merits. Its average benchmark score of 10,883 is actually 4.6% higher than the Tesla C2075's average of 10,400. This is entirely due to the MX350's Vulkan score of 13,077, which is a strong result that the Tesla C2075 cannot match because it has no Vulkan data at all. For users working in Vulkan-based environments, the MX350 is the only viable option from these two.

The percentile rankings are nearly identical: the MX350 sits at the 49th percentile, the Tesla C2075 at the 48th. This suggests that in the broader GPU landscape, they are essentially peers in overall performance, despite their divergent architecture generations. The Tesla C2075's win in OpenCL is a point in its favor, but the MX350's higher average score and Vulkan capability complicate the narrative.

For a buyer or system builder, the decision hinges on workload. If the primary use case is OpenCL compute, the Tesla C2075 is the clear pick. If the workload involves Vulkan rendering or requires a more balanced overall score, the MX350 is the better choice. The data does not support a universal winner; it supports a workload-specific verdict.

Architecture Differences

The two GPUs come from fundamentally different eras of NVIDIA's design philosophy. The GeForce MX350 is built on the Pascal architecture, using the GP107S chip, while the Tesla C2075 is based on the older Fermi 2.0 architecture with the GF110 chip.

The manufacturing process highlights the generational gap. The MX350 uses a 14 nm process from Samsung, while the Tesla C2075 uses a 40 nm process from TSMC. This translates into a dramatic difference in transistor density: the MX350 packs 3,300 million transistors into a 132 mm² die, yielding a density of 25.0 million transistors per mm². The Tesla C2075, by contrast, has 3,000 million transistors spread across a much larger 520 mm² die, resulting in a density of just 5.8 million transistors per mm². The MX350 achieves nearly identical transistor counts in a quarter of the silicon area.

Memory configurations also differ sharply. The MX350 has 2 GB of GDDR5 memory on a 64-bit bus, providing 56.06 GB/s of bandwidth. The Tesla C2075 offers 6 GB of GDDR5 memory on a 384-bit bus, delivering 150.3 GB/s — nearly three times the bandwidth. The memory clock rates also differ: the MX350 runs at 1752 MHz (7 Gbps effective), while the Tesla C2075 runs at 783 MHz (3.1 Gbps effective). The Tesla's wider bus compensates for its slower clock speed.

Compute resources are distributed differently. The MX350 has 640 shading units, 32 texture mapping units, and 16 raster output units. The Tesla C2075 has fewer shading units at 448, but more TMUs (56) and significantly more ROPs (48). The resulting pixel rate for the MX350 is 23.49 GPixel/s, compared to the Tesla's 16.07 GPixel/s. Texture rates are 46.98 GTexel/s for the MX350 versus 32.14 GTexel/s for the Tesla.

Floating-point performance tells a similar story of generational efficiency. The MX350 delivers 1.879 TFLOPS of FP32 compute, while the Tesla C2075 manages 1,027.7 GFLOPS (approximately 1.028 TFLOPS). The MX350 is roughly 83% faster in raw FP32 throughput. The MX350 also has a listed FP16 rate of 29.36 GFLOPS (at a 1:64 ratio), while the Tesla C2075 has no FP16 data at all.

Power and physical requirements diverge dramatically. The MX350 has a TDP of 20 W and requires no power connectors, making it suitable for portable devices. The Tesla C2075 has a TDP of 247 W, is a dual-slot card measuring 248 mm (9.8 inches) in length, requires one 6-pin and one 8-pin power connector, and needs a 550 W power supply. The bus interfaces also differ: the MX350 uses PCIe 3.0 x4, while the Tesla C2075 uses PCIe 2.0 x16.

API support shows a modern advantage for the MX350. It supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. The Tesla C2075 supports DirectX 12 (11_0) and OpenGL 4.6, but has no Vulkan support. Display outputs are portable-device-dependent for the MX350, while the Tesla C2075 offers a single DVI output.

FAQ

Q: Which GPU is faster in OpenCL benchmarks?

A: The NVIDIA Tesla C2075 is faster, scoring 10,400 in Geekbench OpenCL versus 8,689 for the GeForce MX350, a 16.5% lead.

Q: Does the GeForce MX350 have any benchmark advantage over the Tesla C2075?

A: Yes, the MX350 has a higher average benchmark score of 10,883 compared to the Tesla's 10,400, driven by its Geekbench Vulkan score of 13,077, a test where the Tesla has no recorded data.

Q: What are the memory capacities and bandwidths of these two cards?

A: The MX350 has 2 GB of GDDR5 memory with 56.06 GB/s bandwidth on a 64-bit bus. The Tesla C2075 has 6 GB of GDDR5 memory with 150.3 GB/s bandwidth on a 384-bit bus.

Q: Which GPU has better FP32 compute performance?

A: The GeForce MX350 delivers 1.879 TFLOPS, compared to the Tesla C2075's 1,027.7 GFLOPS, making the MX350 approximately 83% faster in FP32 throughput.

Q: What are the power requirements for each card?

A: The MX350 has a 20 W TDP and needs no power connectors. The Tesla C2075 has a 247 W TDP, requires one 6-pin and one 8-pin power connector, and needs a 550 W power supply.

Q: Does the Tesla C2075 support Vulkan?

A: No, the Tesla C2075 has no Vulkan support listed, while the MX350 supports Vulkan 1.4.

Where Each One Wins

NVIDIA GeForce MX350 wins in:

• Overall average benchmark score: 10,883 versus 10,400, a 4.6% advantage.
• Vulkan performance: 13,077 score versus no data for the Tesla.
• FP32 compute: 1.879 TFLOPS versus 1,027.7 GFLOPS.
• Pixel rate: 23.49 GPixel/s versus 16.07 GPixel/s.
• Texture rate: 46.98 GTexel/s versus 32.14 GTexel/s.
• Transistor density: 25.0M / mm² versus 5.8M / mm², indicating far greater design efficiency.
• API support: DirectX 12 (12_1) and Vulkan 1.4 versus DirectX 12 (11_0) and no Vulkan.
• Power efficiency: 20 W TDP versus 247 W TDP, making it suitable for portable devices.

NVIDIA Tesla C2075 wins in:

• OpenCL benchmark: 10,400 versus 8,689, a 16.5% lead.
• Memory capacity: 6 GB versus 2 GB.
• Memory bandwidth: 150.3 GB/s versus 56.06 GB/s.
• Memory bus width: 384-bit versus 64-bit.
• Texture mapping units: 56 versus 32.
• Raster output units: 48 versus 16.
• Physical interface: PCIe 2.0 x16 versus PCIe 3.0 x4, offering a wider connection.

The MX350 is the modern, efficient choice for Vulkan-based workloads and general compute where its higher FP32 throughput and lower power draw are advantageous. The Tesla C2075 is the legacy workhorse for OpenCL-heavy tasks that benefit from its massive memory bandwidth and larger frame buffer, despite its age and higher power consumption. The benchmark data clearly favors the MX350 for overall versatility, but the Tesla C2075's OpenCL dominance is a specific strength that cannot be ignored.