By VRLA Tech  ·  Engineering  ·  April 2026

MATLAB is used across engineering, scientific research, signal processing, control systems, machine learning, and financial modeling. Its hardware requirements vary significantly by workload type — interactive scripting has different demands from large-scale parallel simulation or GPU-accelerated deep learning with the Deep Learning Toolbox. This guide covers the hardware decisions that matter most for MATLAB performance in 2026.

How MATLAB uses hardware by workload

Interactive scripting and data analysis

Interactive MATLAB use — running scripts, plotting data, calling toolbox functions, prototyping algorithms — is primarily single-threaded. High single-core CPU clock speed makes interactive operations feel responsive. The AMD Ryzen 9 9950X at 5.7GHz provides fast interactive MATLAB execution.

Parallel computing with parfor and parfeval

MATLAB’s Parallel Computing Toolbox enables parfor loops and parfeval calls that distribute computation across multiple CPU workers. Each worker uses one CPU core. A 16-core Ryzen 9 9950X runs 16 parallel workers. A 96-core Threadripper PRO 9995WX runs 96 parallel workers. For engineers whose MATLAB code uses parallel loops for Monte Carlo simulations, parameter sweeps, or large-scale signal processing, core count directly determines parallelized throughput.

GPU computing with GPU arrays

MATLAB’s GPU Computing support enables GPU arrays (gpuArray) that execute computations on NVIDIA CUDA GPUs. For matrix operations, FFT computations, and deep learning training with the Deep Learning Toolbox, GPU acceleration can provide 10–100× speedup over CPU for large-scale array operations. MATLAB GPU computing requires an NVIDIA GPU with CUDA support.

Simulink simulation

Simulink model simulation scales with CPU core count for parallel simulation of multiple model variants or parallel Monte Carlo analysis. Large Simulink models with many subsystems and continuous state variables benefit from both high clock speed for single model execution and high core count for parallel runs.

RAM: dataset and model size

MATLAB loads the full workspace into RAM. Large datasets, high-resolution matrices, and Simulink model state all consume RAM. Running out of RAM causes MATLAB to page to disk, dramatically slowing execution. 64GB covers most engineering workflows. 128GB is recommended for large-scale signal processing, genome analysis, financial risk modeling, and large Simulink models with many parallel simulation instances.

Recommended configurations

Engineer / researcher — interactive work and parallel computing

• CPU: AMD Ryzen 9 9950X (high clock + 16 cores for parfor)
• GPU: NVIDIA RTX 5090 (32GB for GPU arrays and Deep Learning Toolbox)
• RAM: 64–128GB DDR5
• NVMe: Fast primary + large data drive

Simulation specialist — large-scale parallel Simulink

• CPU: AMD Threadripper PRO 9995WX (96 cores for large parallel simulation)
• GPU: NVIDIA RTX PRO 6000 Blackwell (ECC for scientific computing)
• RAM: 128–256GB DDR5 ECC

Browse MATLAB workstation configurations on the VRLA Tech Scientific Computing Workstation page.

VRLA Tech has been building custom workstations since 2016. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.