Hardware decisions at a startup have outsized consequences. The wrong choices slow engineers, waste capital, and create maintenance overhead at exactly the wrong time. Here's how to think about workstation procurement for a Nigerian tech startup in 2026.
Know Your Workloads First
Don't buy on job title alone. "Developer" can mean a front-end engineer running VS Code and a browser, or a backend engineer compiling large Rust or C++ codebases, or an ML engineer training models. Each needs dramatically different hardware. Talk to your engineering lead before specifying anything.
The General Developer Workstation (2026)
For web development, mobile development, and general software engineering:
- CPU: AMD Ryzen 7 7700X or Intel Core i7-13700K — strong single-core for editor responsiveness, enough cores for compilation
- RAM: 32GB DDR5 — comfortable for Docker, multiple browser tabs, IDEs simultaneously
- GPU: Integrated or entry RTX (for developers who don't do GPU work)
- Storage: 1TB NVMe SSD
The ML/AI Workstation
For ML engineers: VRAM is everything. An RTX 4090 (24GB) or RTX 5090 (32GB) is the starting point. 128GB system RAM for large datasets. Fast NVMe for dataset storage. This is a significant investment — validate the need before purchasing.
Buy vs Build vs Cloud
For startups: pre-built systems from a trusted builder (like Sephora Systems) have lower hidden costs than self-procurement — the time spent sourcing parts, testing, and managing warranty claims has real cost at a startup. Cloud compute for burst workloads complements local hardware; don't try to buy hardware to eliminate cloud costs entirely.
Standardise Where Possible
Two or three standard configurations (general dev, power dev, ML dev) simplify procurement, maintenance, and IT support. Diversity in hardware specs increases management complexity at every scale.