Fine-Tune AI Models Locally
on Your Own Hardware. For Free.

Yes. FineTuneMyAI is completely free to use for local fine-tuning. You can prepare datasets, train models using LoRA and QLoRA, run evaluations, and perform inference directly on your own hardware (Apple Silicon Mac, Linux CUDA, or Windows) without paying anything.

No. There are no monthly subscription fees, seat licenses, or recurring paywalls for using FineTuneMyAI locally. You only pay if you choose to deploy your fine-tuned models to our managed cloud GPU infrastructure for hosting and inference.

You only pay for cloud GPU compute when hosting your models on FineTuneMyAI managed infrastructure. Hosted inference is billed on a transparent pay-as-you-go basis, starting from $0.99 per GPU hour for ~8B models up to $5.99 per GPU hour for ~70B models.

FineTuneMyAI is proprietary, local-first software. It supports compatible open-weight models such as Llama, Mistral, and Qwen. During local fine-tuning, training data and model artifacts stay on your machine; hosted inference is a separate, optional service that requires the artifacts needed to serve the model.

No. When training locally, base model weights, training corpora, extracted text, embeddings, vector stores, and adapter checkpoints remain strictly on your local disk. The control plane only stores opaque metadata (opaque IDs, token counts, and loss metric floats).

Using 4-bit QLoRA with gradient checkpointing, an 8B model (such as Llama 3.1 or Mistral) can be comfortably trained on an NVIDIA GPU with 12GB-16GB of VRAM or an Apple Silicon Mac with 24GB-36GB of unified memory via our MLX backend.

Your local agent continues training uninterrupted. Training state, optimizer weights, and loss logs are maintained in local JSON state manifests and log files. When connectivity resumes, progress metrics are automatically synchronized.

Device pairing uses an ephemeral 6-digit one-time code and scoped 256-bit cryptographic bearer tokens stored as SHA-256 hashes with HMAC replay protection. The control plane communicates strictly through typed, allowlisted commands (TRAIN_START, EVALUATE_START). Arbitrary shell execution is architecturally forbidden.

The agent program files (agent.js) install to a visible, easy-to-find folder: ~/Documents/finetunemyai/agents on macOS and Linux, and %LOCALAPPDATA%\FineTuneMyAI\agent on Windows. The finetunemyai command wrapper is placed in ~/.local/bin on Unix and %LOCALAPPDATA%\FineTuneMyAI\bin on Windows. Your private device keys and credentials are kept separately in the hidden ~/.finetunemyai folder (and the macOS Keychain / OS keyring) so secrets never sit in a cloud-synced Documents folder. You can override the install location by setting the FINETUNEMYAI_INSTALL_DIR environment variable before running the installer.