GeForce RTX 4090 Laptop vs Apple M2 Max GPU (38-core)

We performed a head-to-head comparison of the GeForce RTX 4090 Mobile 16 GB with 76 pipelines and 9728 shaders against the Apple M2 Max GPU (38-core) that utilizes 608 pipelines and 4864 shaders. Here you will find complete details about specs, efficiency, performance tests, and more.

Some GPUs come in different configurations. For greater precision, pick a specific config below
TGP / Version
-

Review

General comparison of performance in games, applications, power efficiency, and other metrics
Gaming
Performance in DirectX, OpenCL, and Vulkan games
Workstation
Perf. in 3D modeling, video editing and rendering apps
AI/ML
Capabilities for machine learning and AI-related tasks
Energy Efficiency
Power consumption efficiency in different scenarios
NanoReview Final Score
Overall video card score

Key differences

Key distinctions and advantages of M2 Max GPU (38-core) over RTX 4090 Mobile
Reasons to consider the GeForce RTX 4090 Laptop
  • Performs significantly better (up to 2.4x) in 3DMark Steel Nomad Lite
  • 2.9x higher maximum theoretical performance (39.7 vs 13.6 TFLOPS)
  • Includes 16 GB of dedicated GDDR6 memory
  • Supports Nvidia DLSS 3 technology
  • Achieves 2.3x more points in the GeekBench 6 Compute test (203K vs 86K)
  • Has 25% higher memory bandwidth: 512 vs 409.6 GB/s
  • Has 2x more shading units (9728 vs 4864)

Gaming Performance

Frame rate comparison across popular AAA titles at different resolutions

Games

FPS Table
Forza Horizon 5
188
131
122
92
-
The Witcher 3
211
176
132
67
-
Counter-Strike 2
277
217
158
82
-
Far Cry 6
160
144
125
79
-
Hogwarts Legacy
137
111
86
47
-
CoD: Modern Warfare III
177
165
132
84
-
Ghost of Tsushima
122
102
86
52
-
Cyberpunk 2077
151
135
93
44
-
Shadow of the Tomb Raider
250
235
175
96
-
Average FPS by Resolution
1080p High 186 -
1080p Ultra 157 -
1440p Ultra 123 -
4K Ultra 71 -
Margin of Error Low High

Benchmarks

Graphics cards’ performance in recent benchmarking apps

3D Mark

Multiplatform graphics benchmark suite that directly correlates with performance in modern games
Steel Nomad Lite Score
Time Spy 21388 -
Solar Bay 96121 30368
Port Royal 13734 -
Fire Strike 45207 -
Wild Life Extreme 42501 24948
Night Raid 147911 -
Sources: 3DMark [1], [2]

GeekBench 6 OpenCL

GPU test for computational tasks (image processing, photography, computer vision, and ML)
GB6 Compute Score
Background Blur 264 img/sec 149 img/sec
Face Detection 134.3 img/sec 98.1 img/sec
Horizon Detection 9.27 Gpixels/sec 3.8 Gpixels/sec
Edge Detection 12.7 Gpixels/sec 6.88 Gpixels/sec
Gaussian Blur 13.2 Gpixels/sec 4.44 Gpixels/sec
Feature Matching 1.92 Gpixels/sec 0.73 Gpixels/sec
Stereo Matching 1060 Gpixels/sec 264.9 Gpixels/sec
Particle Physics 29225.1 FPS 11144.2 FPS
API OpenCL OpenCL
Sources: Geekbench [3], [4]

Cinebench 2024 GPU

Hardware benchmark using Maxon's Cinema 4D rendering engine
Cinebench 2024 GPU

Passmark Graphics

Videocard test that focuses on compute shaders, multi-texturing, tessellation, and other features
G3D Mark Score
G2D Mark 988 -
DirectX 11 257 FPS -
DirectX 12 106 FPS -
GPU Compute 12157 Ops/s -
Sources: PassMark [5] – 3224 samples

Blender

Rendering performance test for 3D modeling
Blender GPU
7032.52
Sources: Blender [9], [10] – 1065 & 299 samples

Recent User Tests

The latest benchmark tests that have been submitted by users
GeForce RTX 4090 Laptop
DateBenchmarkResult
📘 2026-06-09 (Horikita)Geekbench 6 OpenCL213782
📘 2025-11-03 (JJ)Cinebench 202423277
📘 2025-11-03 (JJ)Cinebench 202422714
Apple M2 Max GPU (38-core)
No benchmark results yet

Artificial Intelligence Tests

Performance in machine learning and artificial intelligence tasks

GeekBench 6 ML

Tests throughput of AI operations in single, half, and quantized precision
GB6 ML Single Precision
GB6 ML Half Precision
GB6 ML Quantized
Image Classification (SP) 11346 6657
Image Segmentation (HP) 24625 13854
Image Super Resolution (Q) 36874 18642
Face Detection (HP) 50088 27965
Pose Estimation (Q) 174844 83144
Text Classification (SP) 3226 2377
Machine Translation (HP) 4905 2052
Object Detection (SP) 14667 6495
Depth Estimation (Q) 53868 31105
Style Transfer (SP) 340056 135686
Framework ONNX Core ML
Backend DirectML GPU
Sources: Geekbench [9], [10]
Results were compared using different ML frameworks

Specifications

Technical specifications of GeForce RTX 4090 Laptop and Apple M2 Max GPU (38-core)

General

Vendor Nvidia Apple
Build Discrete Integrated
Released February 8, 2023 January 17, 2023
Case Laptop Laptop
Purpose Gaming Professional
Segment High-end High-end
Architecture Ada Lovelace Apple M GPU
GPU Codename AD103 (GN21-X11) -
Rival Equivalent - - GeForce RTX 3060 Laptop
Successor - GeForce RTX 5090 Laptop - Apple M5 Max GPU (40-core)
Recommended CPU - Intel Core Ultra 9 185H or above -
Used in CPUs - - Apple M2 Max
Laptop GPU ranking (4th and 40th place)

Graphics Processing Unit

Base Clock 930-1620 MHz (configurable) 450 MHz
Boost Clock 1455-2040 MHz (configurable) 1398 MHz
Shading Units 9728 4864
Texture Mapping Units (TMUs) 304 304
Render Output Units (ROPs) 112 152
Compute Units (Pipelines) 76 608
Tensor Cores 304 -
Ray-tracing Cores 76 -
L1 Cache 128KB per cluster -
L2 Cache 64MB shared -
Instructions Per Cycle 2 IPC 2 IPC

Raw Performance

Pixel Fill Rate 163-228 GPixel/s 212 GPixel/s
Texture Fill Rate 442-620 GTexel/s 425 GTexel/s
FLOPS (FP32)
28.3-39.7 TFLOPS
13.6 TFLOPS

Physical

Interface PCIe 4.0 x16 Custom
TGP 80-175 W (configurable) 70 W
Manufacturing TSMC TSMC
Fabrication Process 5 nm 5 nm
Die Size 379 mm² -
Transistor Count 45.9 billion 52 billion
Transistor Density 121.11 MTr/mm² -
Max. Temperature - 94°C

Memory

Memory Type GDDR6 System Shared
Memory Size 16 GB -
Memory Clock 2000 MHz 6400 MHz
Effective Memory Speed 16000 Mbps 12800 Mbps
Bus 256-bit 512-bit
ECC No No
Memory Bandwidth
512 GB/s

API

DirectX 12 -
Vulkan 1.3 -
OpenGL 4.6 -
OpenCL 3.0 -
CUDA 8.9 -
Ray Tracing Yes No
DLSS DLSS 3 No
DisplayPort 1.4a -

Cast your vote

Choose between two graphics cards
3 (75%)
1 (25%)
Total votes: 4

User opinions

You can share your opinion or ask a question in the comments below
🌐 Register your profile and become part of NanoReview community!