AMD Radeon R9 M375 vs NVIDIA GeForce MX350 Comparison

The NVIDIA GeForce MX350 and AMD Radeon R9 M375 represent two distinct eras of mobile graphics, separated by five years of architectural evolution. The benchmark data presents a split decision: the Radeon R9 M375 dominates in OpenCL compute workloads, while the GeForce MX350 delivers a decisive victory in Vulkan graphics performance. Their average benchmark scores sit remarkably close, with the MX350 at 10883 and the R9 M375 at 10070, placing them in the 49th and 48th percentiles of all GPUs respectively. This near-parity in overall standing, combined with divergent strengths, makes the choice between them entirely dependent on the intended workload.

Where Each One Wins

The AMD Radeon R9 M375 is the clear winner in compute-centric applications that leverage OpenCL. Its Geekbench OpenCL score of 10457 surpasses the MX350’s 8689 by 16.9%, a substantial margin that indicates better raw throughput for general-purpose GPU computing. This advantage suggests the R9 M375 is better suited for tasks like video encoding, data processing, or any workload that can be offloaded to the GPU through OpenCL.

Conversely, the NVIDIA GeForce MX350 is the definitive champion for graphics rendering through Vulkan. Its Geekbench Vulkan score of 13077 crushes the R9 M375’s 9682 by a massive 35.1%. This is a dominant win that highlights the MX350’s superior modern graphics pipeline efficiency. For gaming, 3D rendering, or any application that uses the Vulkan API, the MX350 is unequivocally the stronger performer.

The overall average scores further contextualize this split. The MX350’s average of 10883 is 8.1% higher than the R9 M375’s 10070. However, this average is heavily weighted by the MX350’s explosive Vulkan result, while the R9 M375’s OpenCL win is comparatively smaller in percentage terms. In essence, the R9 M375 offers a more balanced but lower-peak profile, while the MX350 is a specialist in graphics API performance but lags in compute.

Architecture Differences

The fundamental differences between these two GPUs explain their benchmark behavior. The NVIDIA GeForce MX350 is built on the Pascal architecture using a 14 nm process at Samsung’s foundry. This modern node allows for a transistor density of 25.0M per mm², packing 3,300 million transistors into a 132 mm² die. The AMD Radeon R9 M375, in contrast, uses the much older GCN 1.0 architecture on a 28 nm process at TSMC. Its transistor density is a far lower 12.2M per mm², with 1,500 million transistors on a 123 mm² die.

Memory configuration is another critical divider. The MX350 uses 2 GB of GDDR5 memory on a 64-bit bus, achieving a bandwidth of 56.06 GB/s. The R9 M375 also has 2 GB, but it is slower DDR3 memory on a 128-bit bus, resulting in only 28.80 GB/s of bandwidth. This is a significant disadvantage for the AMD part, nearly halving the memory throughput available to the GPU cores. The MX350’s higher bandwidth is likely a key factor in its Vulkan performance, where textures and frame buffers must be moved quickly.

Clock speeds also favor the NVIDIA part. The MX350 has a base clock of 1354 MHz and a boost clock of 1468 MHz, while the R9 M375 operates at 1000 MHz base and 1015 MHz boost. Both have 640 shading units and 16 ROPs, but the R9 M375 has 40 TMUs versus the MX350’s 32. The compute capabilities reflect these differences: the MX350 delivers 1.879 TFLOPS of FP32 performance compared to the R9 M375’s 1,299.2 GFLOPS. The MX350 also supports newer API versions, including DirectX 12 (12_1) and Vulkan 1.4, whereas the R9 M375 is limited to DirectX 12 (11_1) and Vulkan 1.2.170. The power draw is starkly different as well, with the MX350 rated at a 20 W TDP while the R9 M375 has no specified TDP in the data. Finally, the MX350 uses a PCIe 3.0 x4 interface, while the R9 M375 uses a wider x16 connection.

Head-to-Head Benchmarks

The two benchmark results provide a clear, if contrasting, picture of performance. In the Geekbench OpenCL test, the AMD Radeon R9 M375 wins decisively with a score of 10457 against the NVIDIA GeForce MX350’s 8689. The deltaPct of -16.9% indicates the MX350 is nearly 17% slower in this test. This is a major victory for the older AMD architecture and suggests that its GCN compute units, despite lower clocks and less bandwidth, are more efficient in this specific workload.

The Geekbench Vulkan test reverses the outcome dramatically. The NVIDIA GeForce MX350 scores 13077, which is 35.1% higher than the R9 M375’s 9682. This is a crushing defeat for the AMD part and showcases the MX350’s architectural advantages in a modern graphics API. The combination of higher clocks, faster GDDR5 memory, and newer architecture clearly translates into superior Vulkan performance.

Looking at the nearest rivals provides additional context. For the MX350, its closest competitor is the AMD Radeon Pro 450, with a deltaPct of only 0.7%, meaning they are essentially tied in average performance. It also edges out the NVIDIA Quadro K2200 by 1.1% but falls 1.5% behind the GeForce GTX 1650 SUPER and 1.7% behind the Radeon RX 550. For the R9 M375, the rivalry is equally tight, with a 0.3% lead over the Quadro K5100M and a 0.6% lead over the Radeon Pro 5300M. It is 1.1% ahead of the GTX 870M but trails the GTX 950A by 2%. These tight margins among rivals underscore that both GPUs sit in a highly competitive performance tier.

The Verdict

The data presents a clear directive for specific use cases. For users or systems that rely heavily on Vulkan-based applications, the NVIDIA GeForce MX350 is the only rational choice. Its 35.1% lead in Geekbench Vulkan is an overwhelming advantage that cannot be ignored. This makes the MX350 the superior option for modern gaming titles and graphics-intensive software that leverages Vulkan.

Conversely, the AMD Radeon R9 M375 is the better option for OpenCL compute workloads. Its 16.9% lead in Geekbench OpenCL demonstrates a clear edge in this domain. For tasks such as GPU-accelerated video processing, scientific calculations, or other compute-heavy applications that use the OpenCL framework, the R9 M375 delivers more performance.

In a general sense, the MX350 has a higher average benchmark score (10883 vs 10070) and sits in a slightly higher percentile (49 vs 48). However, this overall advantage is almost entirely due to its Vulkan performance. The R9 M375 offers a more competitive OpenCL showing, but its Vulkan deficit is too large to overcome in a balanced assessment. Therefore, unless the primary workload is specifically OpenCL compute, the NVIDIA GeForce MX350 is the more versatile and capable GPU based on the available benchmark data. Its superior memory bandwidth, higher clocks, and modern architecture make it the stronger overall product, despite its significant weakness in one specific API test.

FAQ

Q: Which GPU performs better in Vulkan graphics tests?

A: The NVIDIA GeForce MX350 is significantly better, scoring 13077 in Geekbench Vulkan compared to the AMD Radeon R9 M375’s 9682, a 35.1% advantage.

Q: Does the AMD Radeon R9 M375 have any performance advantage over the MX350?

A: Yes, in OpenCL compute workloads. The R9 M375 scores 10457 in Geekbench OpenCL, which is 16.9% higher than the MX350’s 8689.

Q: How do their overall average benchmark scores compare?

A: The NVIDIA GeForce MX350 has a higher average benchmark score of 10883, while the AMD Radeon R9 M375 averages 10070. This places them in the 49th and 48th percentiles of all GPUs, respectively.

Q: What are the key memory differences between these two GPUs?

A: The MX350 uses 2 GB of GDDR5 memory on a 64-bit bus with 56.06 GB/s bandwidth. The R9 M375 has 2 GB of DDR3 memory on a 128-bit bus but only achieves 28.80 GB/s bandwidth.

Q: Which GPU has higher clock speeds?

A: The NVIDIA GeForce MX350 has significantly higher clocks, with a 1354 MHz base and 1468 MHz boost, compared to the AMD Radeon R9 M375’s 1000 MHz base and 1015 MHz boost.

Q: Are these GPUs closely matched to any rivals in their performance tier?

A: Yes. The MX350 is within 1.7% of the Radeon RX 550 and GTX 1650 SUPER, while the R9 M375 is within 2% of the GTX 950A. Both are also within 1.1% of several other competitors, indicating a very crowded performance segment.