A mini PC can be an excellent development computer, but the right configuration depends on what you build.

Writing Python scripts or editing Shopify themes requires far fewer resources than compiling a large C++ project, running several Docker services or testing applications in multiple virtual machines. Buying the most expensive processor is not always the best answer. In many development workflows, memory capacity, storage space or upgradeability matters more.

Quick Answer

Choose the KAMRUI H2 if your work involves large compilations, parallel test suites or multiple virtual machines.

Choose the KAMRUI AM21 if you want the best balance of upgradeable memory, Radeon 780M graphics and USB4 connectivity.

The KAMRUI G2 Pro is better suited to developers who can work within 32GB of memory but need three NVMe slots, dual 2.5GbE networking or an OCuLink external-GPU upgrade path.

For web development, Python, coursework and lighter local services, the KAMRUI E3B offers a more affordable starting point.

Do not assume that every 32GB mini PC has upgradeable memory. Some configurations use onboard LPDDR memory, so always check the exact processor and RAM variant before purchasing.

Best KAMRUI Mini PCs for Programming

Start With Your Development Workload

The fastest way to choose a mini PC is to identify what normally slows down your current computer.

This prevents two common purchasing mistakes: paying for a powerful GPU that the workload never uses, or choosing a fast CPU with insufficient memory for containers and virtual machines.

How Much RAM Does a Developer Need?

16GB: suitable for lighter development

A 16GB mini PC is generally sufficient for:

• Visual Studio Code;
• web development;
• Python scripting;
• Git and command-line tools;
• university programming assignments;
• remote development through SSH;
• one small local database;
• browser-based IDEs.

It can also run WSL or several lightweight containers, but available memory may become limited when an IDE, browser, database and communication applications remain open simultaneously.

32GB: the practical choice for most professionals

Choose 32GB when your normal workday includes several of the following:

• Docker Desktop;
• Windows Subsystem for Linux;
• JetBrains IDEs or Visual Studio;
• Android Studio;
• a local database;
• several development services;
• many browser tabs;
• one moderate virtual machine.

Google recommends at least 16GB for a good Android Emulator experience. However, Android Studio, an emulator, a browser and Docker running together can justify moving to 32GB. Hardware and VM acceleration must also be enabled for the emulator to perform properly. See Google’s Android Emulator requirements and hardware-acceleration guide.

64GB or more: for virtualisation and large local environments

Consider 64GB or more if you regularly run:

• several complete virtual machines;
• local Kubernetes clusters;
• memory-heavy databases;
• large Java application stacks;
• multiple Android virtual devices;
• local data-processing workloads.

More memory does not automatically make code compile faster. It primarily prevents the operating system from relying heavily on the page file when several memory-intensive tools run at the same time.

How Much SSD Storage Is Enough?

A 512GB SSD is adequate for programming lessons and lightweight web development, but it can fill quickly after installing:

• development SDKs;
• Docker images;
• package caches;
• Android system images;
• local databases;
• virtual-machine disks;
• multiple project repositories.

For a primary development computer, 1TB is a more comfortable starting point.

Developers working with virtual machines, large media assets or local datasets should consider 2TB or a mini PC with multiple M.2 slots.

A second SSD can also separate the operating system from repositories, Docker data, virtual machines or test datasets. This makes storage management easier, although it should not replace a proper backup.

Best for Compilation and Multiple VMs: KAMRUI H2

The KAMRUI H2 offers the strongest potential CPU throughput in this comparison.

It is available with Intel Core i7-14650HX or Core i9-14900HX processors. Intel lists the Core i9-14900HX with 24 cores, 32 threads and a maximum turbo frequency of 5.8GHz. The processor also supports Intel VT-x, VT-d and Extended Page Tables for virtualisation. Intel’s official specifications provide the processor-level details.

The H2 includes:

• 32GB of dual-channel DDR4 memory;
• two SO-DIMM slots;
• support for up to 64GB of RAM;
• a 1TB PCIe 4.0 NVMe SSD;
• two M.2 2280 storage positions;
• HDMI, DisplayPort and USB-C display output;
• Windows 11 Pro.

Choose the Core i7 model for professional web development, business applications, moderate compilation and several containers.

Consider the Core i9 model when large builds, parallel tests or several virtual machines regularly use many processor cores. It is not automatically better value for lightly threaded development because normal code editing will not use all of its additional cores.

The main limitation is Intel UHD integrated graphics. It is sufficient for IDEs, desktop applications and multiple monitors, but it is not designed for demanding game development, CUDA computing or large local AI workloads.

For more product-specific details, see the KAMRUI H2 review.

Best Overall for Most Developers: KAMRUI AM21

The AM21 is the most balanced choice for developers who want capable CPU performance, upgradeable memory and stronger integrated graphics.

Its Ryzen 7 8745HS provides eight cores and 16 threads. The system also includes Radeon 780M integrated graphics, which makes it more suitable than basic integrated graphics for Android emulation, lightweight game-engine work and GPU-accelerated desktop applications.

Its development-friendly features include:

• two DDR5 SO-DIMM slots;
• memory expansion up to 96GB;
• two M.2 2280 storage slots;
• Radeon 780M graphics;
• USB4 connectivity;
• four-display support;
• two Ethernet ports;
• Windows 11 Pro.

The standard 16GB and 512GB configuration is adequate for web development and learning. Developers who regularly use Docker, Android Studio or several local services should consider upgrading to 32GB and 1TB.

Unlike a high-core-count HX system, the AM21 is not specifically optimised for maximum parallel CPU throughput. Its advantage is balance: it can support demanding development tools without sacrificing graphics capability or future memory expansion.

Best for Storage, Networking and eGPU Expansion: KAMRUI G2 Pro

The G2 Pro is designed for developers who need more expansion than a typical mini PC provides.

It combines:

• an eight-core, 16-thread AMD Ryzen 9 270;
• Radeon 780M integrated graphics;
• 32GB LPDDR5-6400 memory;
• a 1TB PCIe 4.0 NVMe SSD;
• three PCIe 4.0 M.2 2280 storage positions;
• dual 2.5GbE Ethernet;
• dual USB4 ports;
• OCuLink for an external GPU;
• four-display support.

This configuration is particularly relevant for developers who run local services, maintain large project datasets or want to separate the operating system, repositories and virtual-machine data across different SSDs.

Dual 2.5GbE connectivity may also be useful for homelabs, network-development environments and systems that access storage over a local network.

The OCuLink port gives the G2 Pro a potential discrete-GPU upgrade path. That matters for developers who may later require CUDA, larger amounts of dedicated VRAM, 3D rendering or more demanding game development.

Best Value for Web Development: KAMRUI E3B

The E3B is a more economical choice for web development, scripting, automation and computer-science coursework.

Depending on the selected configuration, it can support:

• HTML, CSS and JavaScript development;
• Shopify and WordPress development;
• Python scripting;
• Git operations;
• Visual Studio Code;
• local web servers;
• moderate multitasking;
• lightweight Docker environments.

The E3B is sold with different processors and memory arrangements. Some configurations use upgradeable DDR4 SO-DIMM memory, while others use onboard LPDDR memory.

Choose a 32GB configuration if Docker, WSL or several local services are part of the expected workflow. Buyers who only need code editing, a browser and a small local server may be comfortable with 16GB.

Always verify the exact CPU, memory type and storage protocol for the selected SKU rather than assuming every E3B configuration has the same upgrade path.

Budget Choice for Learning and Remote Development: KAMRUI P2

The P2 is most appropriate for introductory programming and remote-development workflows.

It can support:

• programming lessons;
• Visual Studio Code;
• Python and JavaScript;
• Git practice;
• browser-based IDEs;
• SSH connections;
• remote desktop access;
• basic Linux use.

A lower-cost mini PC can make sense when the demanding workload runs on a remote server, cloud development environment or university system. In that situation, local keyboard responsiveness, browser performance and network stability matter more than having a high-end processor.

The P2 is not the right choice for large local builds, several virtual machines or Android emulation. A 16GB configuration is preferable if it will serve as the user’s primary learning computer.

What Is Making Your Development Computer Slow?

Before replacing a computer, identify the actual bottleneck.

The system slows down after Docker starts

This usually points toward memory pressure. Check memory usage before paying for a more powerful processor.

Compilations keep every CPU core busy

A higher-core-count processor such as those offered in the H2 may reduce waiting in well-parallelised builds. The actual improvement depends on the compiler, project and configured power limits.

Android Emulator feels slow

Check that hardware virtualisation and graphics acceleration are enabled. The emulator can perform poorly when it falls back to software rendering.

SDKs, images and virtual machines fill the SSD

Choose at least 1TB of storage or a model with a second M.2 slot. The G2 Pro is particularly notable for offering three M.2 positions.

Local AI tools report insufficient GPU memory

A faster CPU will not solve every AI workload. Larger models and CUDA-based development generally require a compatible discrete GPU with sufficient VRAM.

Git, package downloads or network storage feel slow

Check Ethernet, Wi-Fi, the remote server and SSD before assuming that CPU performance is responsible.

Windows, WSL or Linux?

Choose Windows 11 when:

• developing Windows applications;
• using Visual Studio;
• depending on Microsoft-specific tools;
• requiring Windows-only business software;
• using WSL alongside Windows applications.

Microsoft supports the standard wsl --install process on Windows 10 version 2004 or later and Windows 11. See Microsoft’s WSL installation guide.

Choose Linux when:

• working primarily with server software;
• using open-source toolchains;
• developing containers and infrastructure;
• preferring a native Unix-like environment.

Before replacing Windows, verify support for the exact Wi-Fi adapter, Ethernet controller, audio hardware, display outputs and sleep functions.

Using Hyper-V

Full Hyper-V support requires Windows 10 or 11 Pro or Enterprise, a compatible 64-bit processor, SLAT and hardware virtualisation enabled in the BIOS or UEFI. Microsoft also notes that a host requires enough memory for Windows and every concurrently running VM. See the official Hyper-V requirements.

Is a Mini PC Suitable for Local AI Development?

A mini PC can run AI-assisted coding tools and smaller local models, but a high CPU core count or an NPU specification does not make it a replacement for a dedicated AI workstation.

Integrated graphics use shared system memory rather than dedicated VRAM. Small quantised models may work, while larger models, longer contexts and GPU-training workloads generally require a discrete GPU.

The H2 is better suited to compiling AI-related software, preprocessing data and accessing cloud AI services than to acting as a CUDA workstation.

The AM21 and G2 Pro provide stronger Radeon 780M integrated graphics, but developers should confirm support in their intended framework.

If future discrete-GPU support matters, the G2 Pro’s OCuLink connection gives it a more relevant upgrade path. However, the cost of an eGPU dock, desktop graphics card and power supply should be included in the final budget.

Who Should Not Buy a Mini PC for Programming?

A mini PC may not be the right primary development machine if you need:

• several internal PCIe expansion cards;
• multiple full-size hard drives;
• a high-end discrete GPU inside the chassis;
• large amounts of dedicated GPU memory;
• 128GB or more of system memory;
• maximum component-level repairability;
• sustained workstation-class rendering or AI training.

Game developers working with large Unreal Engine projects, AI developers who depend on CUDA and professionals who need several PCIe devices may be better served by a full-size desktop workstation.

Frequently Asked Questions

Is 16GB of RAM enough for programming in 2026?

Yes, for web development, Python, coding lessons, remote development and smaller projects. Choose 32GB when Docker, Android Studio, WSL, large IDEs or multiple local services regularly run together.

Is 32GB enough for Docker and WSL?

It is a practical amount for many professional workflows involving an IDE, several containers, WSL and a local database. Large container environments or local Kubernetes clusters may require 64GB or more.

Can a mini PC run Android Studio?

Yes. Choose a modern processor, at least 16GB of memory and hardware-virtualisation support. A 32GB configuration provides more headroom when the emulator, IDE, browser and containers run simultaneously.

Do programmers need a dedicated GPU?

Most web, backend, mobile and business-application developers do not. A discrete GPU becomes important for CUDA, advanced game development, 3D rendering and larger local AI models.

Is the H2 or AM21 better for programming?

Choose the H2 for CPU-heavy compilation and multiple virtual machines. Choose the AM21 when upgradeable memory, Radeon 780M graphics and USB4 provide a better balance for the workflow.

Is the G2 Pro better than the AM21 for developers?

The G2 Pro is better for three-SSD storage, dual 2.5GbE networking and OCuLink eGPU expansion. The AM21 is better when future memory expansion beyond 32GB matters.

Is a Mini PC suitable for computer-science students?

Yes. An E3B or P2 can be sufficient for introductory programming, Python, JavaScript and coursework. Students expecting to use Android Studio, Docker or several local services should choose at least 32GB or an upgradeable model.

Final Recommendation

There is no single best mini PC for every programmer.

Choose the KAMRUI H2 when compilation speed, parallel testing and virtual-machine capacity are the priorities.

Choose the AM21 for the strongest balance of upgradeable RAM, modern integrated graphics, storage expansion and connectivity.

Choose the G2 Pro when 32GB is sufficient and the workflow benefits from three NVMe drives, dual 2.5GbE networking or an external-GPU upgrade path.

Choose the E3B for web development, Python and everyday coding, or the P2 for learning and remote development.

The most important rule is simple: buy for the workload that creates the bottleneck. Prioritise CPU cores for parallel compilation, memory for containers and virtual machines, storage for SDKs and VM images, and a compatible discrete GPU for demanding AI or 3D development.

Still deciding? Compare the current KAMRUI Mini PC range by processor, memory upgradeability, storage and graphics capability.