AI Builder Workflows

Single model, code completion

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.

Dedicated coding model + embeddings

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.

Concurrent coding + reasoning + embeddings

The complete local AI development stack: concurrent coding model + reasoning model + embeddings. Switch between QwQ for architecture decisions and Qwen Coder for implementation, with local RAG always available.

Apple Silicon unified memory

The silent, unified-memory approach: Apple Silicon with enough memory to run coding + reasoning + embeddings concurrently. No fan noise, no separate GPU. The premium option for builders who value silence and simplicity.

Always-on, multi-user household AI

Always-on local AI server for a household or small team. Runs Ollama + Open WebUI accessible from any device on the network. Serves chat, coding assistance, document Q&A, and transcription to multiple simultaneous users — with zero API costs and complete data privacy.

Fine-tune language models locally with QLoRA, LoRA, and full fine-tuning. Train custom adapters for domain-specific tasks without sending proprietary data to third-party APIs. VRAM requirements scale with model size and method: QLoRA fine-tuning a 7B model fits in 16GB, while full fine-tuning of 32B models needs 48GB+. System RAM matters — gradient checkpointing and dataset loading use 2-4x the model's VRAM in system memory.

Local image and video generation with FLUX.1, SDXL, and Stable Diffusion 3. Run ComfyUI workflows with LoRAs, ControlNet, and upscaling — no cloud credits, no content filters, no rate limits. VRAM is the bottleneck: FLUX.1 at full quality needs 24GB, but Q4 quantization brings it to 8GB GPUs.