Question 1

Can I fine-tune LLMs with FrameTrain?

Accepted Answer

Yes. FrameTrain supports fine-tuning of Large Language Models (LLMs) via LoRA and QLoRA. You can import models like Llama, Mistral, or Phi directly from HuggingFace and adapt them to your own datasets.

Question 2

Which GPUs are supported?

Accepted Answer

FrameTrain runs on all NVIDIA GPUs with CUDA support (from the GTX 10 series onward) as well as on Apple Silicon (M1/M2/M3/M4) via Metal Performance Shaders. Pure CPU-based training is also possible.

Question 3

What's the difference compared to Google Colab or AWS SageMaker?

Accepted Answer

FrameTrain runs entirely locally on your machine. There are no ongoing cloud costs, your data is never uploaded, and you're not dependent on an internet connection. Ideal for sensitive or proprietary datasets.

Question 4

Do I need programming knowledge?

Accepted Answer

No. FrameTrain is designed as a graphical application. You configure training parameters through an intuitive interface without writing any code. Advanced users can also use configuration files.

Question 5

Does FrameTrain support LoRA training?

Accepted Answer

Yes, LoRA (Low-Rank Adaptation) is fully integrated. You can configure LoRA rank, alpha, and target modules directly in the app, which also enables fine-tuning on GPUs with limited VRAM.

Question 6

Why does FrameTrain only cost €4.99/month?

Accepted Answer

FrameTrain is in Early Access. The price is intentionally low so that as many developers and researchers as possible can get access early. The price increases to €9.99 after the first 100 users – sign up now to lock in the lower price permanently.

Question 7

Does FrameTrain run on Apple M1/M2/M3/M4?

Accepted Answer

Yes. FrameTrain natively supports Apple Silicon via Metal Performance Shaders (MPS). Models are accelerated on the M-chip's GPU without requiring CUDA.

Question 8

Which model formats are supported?

Accepted Answer

FrameTrain supports PyTorch models (.pt, .bin), SafeTensors, and the GGUF format. Models can be imported directly from the HuggingFace Hub or loaded locally.

Question 9

Can I use my own datasets?

Accepted Answer

Yes. You can load your own datasets in CSV, JSON, or JSONL format. FrameTrain also supports datasets directly from the HuggingFace Datasets Hub.

Question 10

Does FrameTrain support mixed precision and gradient checkpointing?

Accepted Answer

Yes. FP16 and BF16 mixed-precision training are fully supported, as is gradient checkpointing for memory-efficient training on GPUs with limited VRAM.

Guides

LoRA Fine-Tuning Explained

Local vs Cloud – The Honest Comparison

Best GPU for ML Training 2026