Home / GPU Comparison / NVIDIA GeForce GTX 1650 Max Q or NVIDIA GeForce MX350: What's better?

NVIDIA GeForce GTX 1650 Max Q
vs
NVIDIA GeForce MX350

NVIDIA GeForce GTX 1650 Max Q
vs
NVIDIA GeForce MX350

GPU Comparison Result

Below are the results of a comparison of NVIDIA GeForce GTX 1650 Max Q and NVIDIA GeForce MX350 video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA GeForce GTX 1650 Max Q
NVIDIA GeForce GTX 1650 Max Q
NVIDIA GeForce GTX 1650 Max Q
  • Higher Boost Clock: 1125MHz (1125MHz vs 937MHz)
  • Larger Memory Size: 4GB (4GB vs 2GB)
  • Higher Bandwidth: 160.0 GB/s (160.0 GB/s vs 56.06 GB/s)
  • More Shading Units: 1024 (1024 vs 640)
  • Newer Launch Date: April 2020 (April 2020 vs February 2020)

Basic

NVIDIA
Label Name
NVIDIA
April 2020
Launch Date
February 2020
Mobile
Platform
Mobile
GeForce GTX 1650 Max Q
Model Name
GeForce MX350
GeForce 16 Mobile
Generation
GeForce MX
930MHz
Base Clock
747MHz
1125MHz
Boost Clock
937MHz
PCIe 3.0 x16
Bus Interface
PCIe 3.0 x4
4,700 million
Transistors
3,300 million
64
TMUs
?
Texture Mapping Units (TMUs) serve as components of the GPU, which are capable of rotating, scaling, and distorting binary images, and then placing them as textures onto any plane of a given 3D model. This process is called texture mapping.
32
TSMC
Foundry
Samsung
12 nm
Process Size
14 nm
Turing
Architecture
Pascal

Memory Specifications

4GB
Memory Size
2GB
GDDR6
Memory Type
GDDR5
128bit
Memory Bus
?
The memory bus width refers to the number of bits of data that the video memory can transfer within a single clock cycle. The larger the bus width, the greater the amount of data that can be transmitted instantaneously, making it one of the crucial parameters of video memory. The memory bandwidth is calculated as: Memory Bandwidth = Memory Frequency x Memory Bus Width / 8. Therefore, when the memory frequencies are similar, the memory bus width will determine the size of the memory bandwidth.
64bit
1250MHz
Memory Clock
1752MHz
160.0 GB/s
Bandwidth
?
Memory bandwidth refers to the data transfer rate between the graphics chip and the video memory. It is measured in bytes per second, and the formula to calculate it is: memory bandwidth = working frequency × memory bus width / 8 bits.
56.06 GB/s

Theoretical Performance

36.00 GPixel/s
Pixel Rate
?
Pixel fill rate refers to the number of pixels a graphics processing unit (GPU) can render per second, measured in MPixels/s (million pixels per second) or GPixels/s (billion pixels per second). It is the most commonly used metric to evaluate the pixel processing performance of a graphics card.
14.99 GPixel/s
72.00 GTexel/s
Texture Rate
?
Texture fill rate refers to the number of texture map elements (texels) that a GPU can map to pixels in a single second.
29.98 GTexel/s
4.608 TFLOPS
FP16 (half)
?
An important metric for measuring GPU performance is floating-point computing capability. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy.
18.74 GFLOPS
72.00 GFLOPS
FP64 (double)
?
An important metric for measuring GPU performance is floating-point computing capability. Double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy, while single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
37.48 GFLOPS
2.35 TFLOPS
FP32 (float)
?
An important metric for measuring GPU performance is floating-point computing capability. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
1.175 TFLOPS

Miscellaneous

16
SM Count
?
Multiple Streaming Processors (SPs), along with other resources, form a Streaming Multiprocessor (SM), which is also referred to as a GPU's major core. These additional resources include components such as warp schedulers, registers, and shared memory. The SM can be considered the heart of the GPU, similar to a CPU core, with registers and shared memory being scarce resources within the SM.
5
1024
Shading Units
?
The most fundamental processing unit is the Streaming Processor (SP), where specific instructions and tasks are executed. GPUs perform parallel computing, which means multiple SPs work simultaneously to process tasks.
640
64 KB (per SM)
L1 Cache
48 KB (per SM)
1024KB
L2 Cache
512KB
30W
TDP
20W
1.3
Vulkan Version
?
Vulkan is a cross-platform graphics and compute API by Khronos Group, offering high performance and low CPU overhead. It lets developers control the GPU directly, reduces rendering overhead, and supports multi-threading and multi-core processors.
1.3
3.0
OpenCL Version
3.0
4.6
OpenGL
4.6
12 (12_1)
DirectX
12 (12_1)
7.5
CUDA
6.1
None
Power Connectors
None
32
ROPs
?
The Raster Operations Pipeline (ROPs) is primarily responsible for handling lighting and reflection calculations in games, as well as managing effects like anti-aliasing (AA), high resolution, smoke, and fire. The more demanding the anti-aliasing and lighting effects in a game, the higher the performance requirements for the ROPs; otherwise, it may result in a sharp drop in frame rate.
16
6.6
Shader Model
6.4

Benchmarks

FP32 (float) / TFLOPS
GeForce GTX 1650 Max Q
2.35 +100%
GeForce MX350
1.175
3DMark Time Spy
GeForce GTX 1650 Max Q
3000 +138%
GeForce MX350
1262
Blender
GeForce GTX 1650 Max Q
375 +284%
GeForce MX350
97.72
OctaneBench
GeForce GTX 1650 Max Q
67 +131%
GeForce MX350
29