Compute Options for Fine-Tuning

Overview

Two practical paths for fine-tuning a 7B–13B specialist LLM without building a GPU server: Apple Silicon Macs (Mac Mini or MacBook Pro with 32–128 GB unified memory) and rented GPU cloud instances (RunPod, Lambda Labs, Vast.ai). This section covers both paths with realistic time and cost estimates.

Entries

  • Apple Silicon Fine-Tuning — Mac Mini M4 Pro/Max and MacBook Pro: MLX, Unsloth, capabilities, and realistic throughput
  • Cloud GPU Fine-Tuning — RunPod, Lambda Labs, Vast.ai: instance types, costs per hour, and time-to-completion estimates

Entries

  • Apple Silicon for LLM Fine-Tuning — Using Mac Mini M4 Pro/Max or MacBook Pro for QLoRA and MLX-based LLM fine-tuning: supported tools, memory requirements, realistic training speeds, and expected time-to-completion for a 10,000-example network engineer dataset.
  • Cloud GPU Fine-Tuning — Renting GPU instances on RunPod, Lambda Labs, and Vast.ai for LLM fine-tuning: instance types, current pricing, realistic time-to-completion, and cost estimates for a 10,000-example network engineer dataset.