Power Coding Workflow

power

Run a dedicated 32B coding model with embeddings for local RAG. The sweet spot for developers who want serious local AI coding without API dependency.

CursorContinueAiderOllamaOpen WebUI

Concurrent VRAM

18.9 GB

Peak VRAM

18.9 GB

Min Bandwidth

400 GB/s

Models Required

VRAM Breakdown

How the 18.9 GB concurrent VRAM is used.

Always Running (Concurrent)

Qwen 2.5 Coder 32B Instruct(code completion)

18.4 GB

Q4_K_M · 32.5B

nomic-embed-text v1.5(embeddings for rag)

512 MB

FP16 · 137M

Local vs API Costs

Typical Monthly API Cost

$80/mo

Break-Even Point

12 months

Annual Savings After Break-Even

~$768/yr

Based on Cursor Pro ($20/mo) + ~500 API completions/day with a 32B-class model (~$2/day). AI Builder Workstation at $2,902. Break-even includes electricity cost (~$15/mo at 6hr/day usage).

Recommended Builds

Pre-configured builds that can run the Power Coding Workflow workflow.

Mid-Range

Mid-RangeFull AI Builder

AI Builder Workstation

NVIDIA GeForce RTX 4090 · 64GB DDR5-5600 (2x32GB)

$2,90224 GB VRAM

Runs 10 models

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

Get a personalized recommendation for this workflow →

Author: Ada. Last updated: 2026-03-01.