Computer scientists avoid hardware quirks by using the Random Access Machine (RAM) model, an idealized abstraction that treats memory as an infinite array of cells with random access in constant time. Within this framework, algorithmic claims like “runs in O(n log n)” or uses O(1) extra space become machine‑independent today.

The model comprises four parts: an unbounded memory, a single‑instruction CPU with a few registers, a sequential program, and simple I/O. It assumes unit cost for every arithmetic, logical, or memory operation, though a logarithmic cost variant accounts for larger operands. Summing an array illustrates a 3n + 2 step O(n) analysis.

Despite ignoring caches, parallelism, and fixed word sizes, the RAM model remains useful because growth‑rate predictions hold across platforms. Researchers extend it with PRAM, External Memory Model, Cache‑Oblivious Model, and Word RAM to capture parallel execution or hierarchy effects. Future work watches how these variants influence real‑world performance guarantees.