Contents13
- The short answer — 16 GB RAM, Core i5 / Ryzen 5 or better, around 100,000 yen
- Use 16 GB of RAM as the baseline
- When 32 GB is warranted
- Core i5 / Ryzen 5 or better — and check the generation
- Choosing between i5 and Ryzen 5
- Picks by use case — learning, side project, machine learning
- Learning (Progate / Udemy → portfolio work)
- Side project / career change (closer to real work, running Docker and VMs)
- Machine learning (training locally)
- Comparison — Windows laptop, MacBook, Linux laptop
- Adding a monitor pays off more than upgrading the laptop
- FAQ
- Wrapping up
2026 update: ported from the old VuePress blog. The framework still holds, but the CPU generations and prices are pinned to when this was written. Confirm current generations and stock before you buy.
The first thing that trips people up when starting to learn programming is picking the laptop. Which numbers on the spec sheet matter, and how far is it worth spending before the extra money stops paying off — it’s hard to tell.
This piece lays out the spec criteria for buying a Windows laptop, assuming web-focused learning, a side project, or a career change. For readers torn between this and a MacBook, there’s a comparison table further down.
The short answer — 16 GB RAM, Core i5 / Ryzen 5 or better, around 100,000 yen
Short version: for learning or side-project use, pick a model that clears 16 GB RAM, Core i5 (11th gen or newer) or Ryzen 5 (4th gen or newer), 512 GB SSD. The price band is around 100,000 yen.
Two reasons:
- The moment you have an IDE, a browser, and Docker or a VM running together, 8 GB is cramped. 16 GB carries you a few years
- A mid-tier CPU (i5 / Ryzen 5) handles IDE builds and test runs without felt stress
Going beyond i7 / Ryzen 7 or 32 GB of RAM is overkill unless you’re also doing machine learning or video editing. The cost-to-benefit drops off.
If you’re a MacBook person, there’s a separate piece for that.
How to pick a MacBook for programming
Use 16 GB of RAM as the baseline
Short version: 16 GB. 8 GB works at the very start of learning, but you hit the ceiling within six months to a year.
The things that eat RAM while you code:
- IDE (VS Code, IntelliJ, and so on) — several GB once extensions are in
- Browser tabs — a dozen-plus is normal while developing
- Docker / VMs — a few hundred MB per container, upward
- Local DB / dev server — running alongside everything else
Run those in parallel and 8 GB is gone. Once swap kicks in it eats at SSD lifespan too, so putting 16 GB in from day one is cheaper in the end.
When 32 GB is warranted
If you’re also doing machine learning, large-scale data processing, or video editing, go to 32 GB. For plain web development or Android-leaning mobile work, 16 GB is enough.
Core i5 / Ryzen 5 or better — and check the generation
Short version: pick a mid-tier (i5 / Ryzen 5) chip from a recent generation. The generation gap matters more than the tier gap, in feel.
Realistic floor for laptop CPUs (as of 2021):
- Intel: 11th-gen Core i5 or newer
- AMD: Ryzen 5 4th gen (5000 series) or newer
By 2026 the generations have moved on, so as long as you stay within the latest two generations at purchase, you won’t go wrong.
Choosing between i5 and Ryzen 5
For programming use, the felt difference is close to zero. Rules of thumb:
- More cores at the same price → Ryzen 5
- Better battery life and power efficiency → Intel Core i5
- Enterprise support and stable stock → Intel Core i5
i7 / Ryzen 7 and above belong to the “also doing ML or video editing” bucket. For programming alone, the upside is thin.
Picks by use case — learning, side project, machine learning
Short version: the three patterns differ a lot. Price bands and required specs are not interchangeable.
Learning (Progate / Udemy → portfolio work)
- CPU: Core i5 / Ryzen 5 (mid)
- RAM: 16 GB
- SSD: 512 GB
- GPU: integrated is enough
- Price target: 80,000–100,000 yen
This is plenty for a first machine. Outgoing-model and direct-sales outlets can knock another 20,000–30,000 yen off.
Side project / career change (closer to real work, running Docker and VMs)
- CPU: Core i5 / Ryzen 5 (recent mid-tier)
- RAM: 16 GB (ideally a model you can later upgrade to 32 GB)
- SSD: 512 GB to 1 TB
- GPU: integrated is enough
- Price target: 100,000–130,000 yen
You’ll start running Docker, multiple VMs, and a local DB at the same time. Confirm whether RAM is upgradeable.
Machine learning (training locally)
- CPU: Core i7 / Ryzen 7
- RAM: 32 GB or more
- SSD: 1 TB
- GPU: NVIDIA GeForce RTX class (8 GB VRAM or more)
- Price target: 150,000 to over 250,000 yen
Honestly, finishing everything on a laptop is rough. Once the training workload passes a certain size, it’s more realistic to plan around cloud GPU from the start.
Comparison — Windows laptop, MacBook, Linux laptop
Short version: at equivalent specs for web or backend work, Windows is the cheapest. If you want Apple Silicon’s power efficiency, MacBook. If you want the hardware to be your dev environment, a Linux laptop.
| Aspect | Windows laptop | MacBook (Apple Silicon) | Linux laptop (ThinkPad and so on) |
|---|---|---|---|
| Price at equivalent specs | Cheapest (baseline) | +20,000–30,000 yen | On par with Windows |
| Dev environment setup | WSL2 covers most | Native (UNIX-like macOS) | Native, straightforward |
| iOS / macOS development | No (Xcode won’t run) | Yes | No |
| Battery / quietness | Depends on the model | Strong | Depends on the model |
| Expandability / repair | Easier (model dependent) | Harder | Easier |
| Machine learning (GPU) | NVIDIA-equipped models available | Via MPS / Metal (limited) | NVIDIA-equipped models available |
| Learning resources | Plenty (workplaces run Windows) | Plenty | Fewer |
Unless iOS development is on the table, Windows is a safe pick. If you want a native Linux experience, a ThinkPad or Dell XPS with Ubuntu installed is the steady choice.
Adding a monitor pays off more than upgrading the laptop
Adding a single external monitor moves the needle on day-to-day work more than bumping the laptop one tier. Whether you can lay out code next to a browser (preview, docs) feeds directly into productivity.
For lightweight picks aimed at writing away from home, there’s a separate piece.
FAQ
Q. Is 8 GB of RAM enough for programming? A. For learning or small personal projects, 8 GB will run. The moment you bring up an IDE, a browser with lots of tabs, and Docker or a VM at the same time, it gets tight fast. Unless you plan to upgrade soon anyway, picking 16 GB from the start is cheaper in the long run.
Q. MacBook or Windows for programming? A. If you’re not building iOS apps, either works. At equivalent specs Windows runs 20,000–30,000 yen cheaper, and WSL2 covers most Linux-side development just fine. It’s a choice between Apple Silicon’s power efficiency and Windows’ price and expandability.
Q. Core i5 or Ryzen 5 — which should I pick? A. For programming use, the felt difference is negligible. At the same price, Ryzen 5 usually wins on core count, while Intel tends to be steadier on battery life and power efficiency. Decide on price, stock, and battery requirements.
Q. What kind of build do I need for machine learning? A. If you want to train locally, the realistic floor is an NVIDIA GPU (8 GB VRAM or more) plus 32 GB of RAM. If you’re serious about it, paying for cloud GPU (Colab, RunPod, and so on) ends up cheaper. I wouldn’t recommend trying to finish everything on a laptop.
Wrapping up
For a programming Windows laptop, 16 GB RAM, Core i5 / Ryzen 5 or better, 512 GB SSD, around 100,000 yen is hard to go wrong with. Past that line, the call comes down to whether you also need machine learning or video editing.
If you’re torn between this and a MacBook, the dividing line is iOS development. If you’re not doing it, Windows at the same specs for 20,000–30,000 yen less is plenty.
If the budget can stretch to an external monitor, buying one is a bigger felt jump than bumping the laptop itself a tier.
