AI Workflow

Basic Coding Assistant

basic

Run a single local coding model for code completion and chat. The entry-level builder setup: replace API-dependent code completion with a local 7-8B model.

CursorVS CodeContinueLM StudioOllama

Concurrent VRAM

5.8 GB

Peak VRAM

5.8 GB

Min Bandwidth

200 GB/s

Models

1

Memory

VRAM Breakdown

How the 5.8 GB concurrent VRAM is used.

Switched (Loaded As Needed)

These share VRAM with the largest concurrent model. Only one runs at a time.

Llama 3.1 8B Instruct(code completion and chat)
5.8 GB

Q5_K_M

Buying Priority

What matters most for this workflow

This workflow fits on surprisingly modest hardware, so the main decision is whether you want the cheapest workable setup or enough headroom to keep the experience snappy.

Practical Tradeoff

How to think about the hardware

Treat this as a workflow where convenience and control matter more than raw ROI. Local hardware still makes sense, but the win is predictable latency and ownership, not just monthly cost savings.

Return on Investment

Local vs API Costs

Typical Monthly API Cost

$30/mo

Break-Even Point

25 months

Annual Savings

~$288/yr

Based on ~200 Cursor completions/day at ~$1/day API cost. Budget AI Desktop at $753. Privacy and offline access are the main value drivers at this tier, not pure cost savings.

Hardware

Recommended Builds

Pre-configured builds that can run the Basic Coding Assistant workflow.

Prefer a Mac? Apple Silicon with unified memory can run this workflow too. See the Mac AI Builder workflow β†’

Build my rig for this workflow β†’