Fine-Tune AI Models
Locally on Your Mac
FrameTrain runs LoRA and QLoRA fine-tuning directly on Apple Silicon using Apple’s Metal Performance Shaders (MPS). No NVIDIA GPU, no CUDA, no cloud – your data and your models stay on your Mac.
Why Apple Silicon is great for local training
Apple Silicon chips (M1, M2, M3, M4) combine CPU, GPU and Neural Engine with unified memory: the GPU can address the full system memory instead of a small dedicated VRAM pool. A 32 GB Mac can therefore work with models that would need a much more expensive NVIDIA card.
FrameTrain talks to the GPU through Metal MPS, the same acceleration backend PyTorch uses on macOS. You get GPU-accelerated training without CUDA – and everything runs offline, which keeps it GDPR-compliant by design.
No CUDA required
Unified memory
GPU and CPU share one memory pool – more of your RAM is usable for the model than on a comparable discrete GPU.
Metal MPS acceleration
PyTorch runs on Apple’s Metal backend, so training is GPU-accelerated on every M-series Mac out of the box.
Fully local & private
Datasets and trained models never leave your machine. No cloud account, no upload, no data-processing agreement needed.
What can your Mac handle?
A practical orientation by unified memory. These are guidance estimates, not fixed benchmarks – real feasibility depends on quantization, sequence length, batch size and the base model.
| Mac / Chip | Unified memory | Realistic use |
|---|---|---|
| MacBook Air M1 / M2 | 8 GB | Classic ML models, small networks, quantized 1–3B LoRA experiments |
| MacBook Pro M2 / M3 | 16 GB | LoRA / QLoRA fine-tuning of 7B models (quantized), small full fine-tunes |
| MacBook Pro M3 / M4 Pro | 18–24 GB | Comfortable 7B LoRA & QLoRA fine-tuning, larger datasets |
| MacBook Pro / Studio M-series Max | 32–64 GB | 7B–13B fine-tuning, longer sequences, faster iteration |
| Mac Studio M-series Ultra | 64–192 GB | 13B+ models and several experiments in parallel |
Rule of thumb: QLoRA (4-bit) roughly halves the memory a model needs versus standard LoRA, so a tier above becomes reachable.
How training works on a Mac
- 1
Pick a model
Import a Hugging Face model or load a local one – directly in the GUI.
- 2
Prepare your dataset
Import, split into train/validation/test and validate it before training.
- 3
Configure LoRA / QLoRA
Set rank, learning rate and epochs – with sensible defaults for Apple Silicon.
- 4
Train on MPS
Training runs on the Metal GPU with a live loss curve and training analysis.
- 5
Version & export
Every run is versioned; export the result (incl. GGUF) or run it locally via Ollama.
Requirements
- Apple Silicon Mac (M1, M2, M3 or M4 – any variant)
- macOS 13 Ventura or newer
- 8 GB unified memory minimum – 16 GB+ recommended for 7B LLMs
- Enough free disk space for models and checkpoints (10–50 GB depending on model)
- No NVIDIA GPU and no CUDA needed
Apple Silicon FAQ
Does FrameTrain support Apple Silicon (M1, M2, M3, M4)?
Yes. FrameTrain runs natively on all Apple Silicon chips and uses Metal MPS for GPU-accelerated training. Intel Macs are not the focus – an M-series chip is recommended.
Do I need an NVIDIA GPU or CUDA to train on a Mac?
No. On macOS, FrameTrain accelerates training through Apple’s Metal backend (MPS). CUDA is only relevant on Windows/Linux machines with NVIDIA GPUs.
Can I fine-tune a 7B LLM on my Mac?
Typically yes from about 16 GB of unified memory upward, using LoRA or QLoRA. With 32 GB or more you can also work with larger models and longer sequences.
Is training on Apple Silicon slower than on an NVIDIA GPU?
For heavy workloads a high-end NVIDIA GPU is usually faster. Apple Silicon’s advantage is unified memory and running everything locally and quietly on a laptop – for LoRA/QLoRA fine-tuning it is very capable.
Train on your Mac – today
Download FrameTrain and start your first local fine-tune on Apple Silicon in minutes.