Tech & Tools

How to Train an AI Model: From Scratch to RunPod

A practical guide to training and fine-tuning AI models – from no-code tools for beginners to GPU cloud training with RunPod.

14 min readUpdated: February 8, 2026

TrainingFine-TuningRunPodLoRALLMAxolotl

01Overview: How AI Model Training Works

Training an AI model means teaching it patterns from data. In practice, you rarely train from scratch: that requires huge datasets and massive compute. Most users either use no-code tools for simple models (e.g. image or sound classifiers) or fine-tune existing foundation models (LLMs, diffusion models) on their own data. Fine-tuning updates only part of the parameters and is far more efficient.

02Easiest Options for Beginners (No Code)

If you want to train a small model without programming, these options are the simplest:

Google Teachable Machine: Free, browser-based. Train image, sound, or pose classifiers by uploading examples. No code, no GPU required. Ideal for prototypes and learning.
Machine Learning for Kids: Train on images, sounds, text, or numbers with a simple interface. Good for education and small projects.
MIT App Inventor: Build mobile apps that use your trained models. Combines visual app design with ML.

03Training from Scratch vs. Fine-Tuning

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Training from scratch means initializing all parameters randomly and learning everything from your dataset. It needs large amounts of data and lots of GPU time. Fine-tuning starts from a pre-trained model (e.g. Llama, SDXL) and adapts it with your data. Parameter-efficient methods like LoRA or QLoRA only train a small number of extra parameters, which saves memory and cost. For most use cases – custom LLM behavior, domain-specific images, or style LoRAs – fine-tuning is the right choice.

04RunPod: GPU Cloud for Serious Training

RunPod provides on-demand GPU instances (Pods) and serverless GPUs in many regions. It is well-suited for fine-tuning LLMs and diffusion models when you need more power than a single PC or want to avoid managing your own hardware. You get full control over the environment, pay per use, and can scale to multi-GPU or multi-node clusters for large models.

05Fine-Tuning an LLM on RunPod with Axolotl

RunPod’s recommended path for LLM fine-tuning is Axolotl, which works with Hugging Face models and datasets. Summary of the steps:

Prerequisites: RunPod account; optionally a Hugging Face token for gated models.
Base model: In RunPod’s Fine Tuning section, enter a Hugging Face model ID (e.g. NousResearch/Meta-Llama-3-8B). For gated models, add your HF token.
Dataset: Pick a dataset on Hugging Face (e.g. tatsu-lab/alpaca) and enter its ID in the Dataset field.
Deploy Pod: Click “Deploy the Fine Tuning Pod” and choose a GPU (smaller models: less VRAM; 70B-class: 80GB or multi-GPU). Wait until the environment is ready.
Connect: Use the Pod’s Jupyter Notebook, Web Terminal, or SSH to access the machine.
Configure: In /workspace/fine-tuning/ you’ll find config.yaml (and examples). Adjust base_model, dataset path, output_dir, learning_rate, num_epochs, micro_batch_size, gradient_accumulation_steps, and optionally Weights & Biases logging.
Train: Run ‘axolotl train config.yaml’ and monitor the terminal. Outputs go to the configured output_dir (e.g. ./outputs/out).
Share: Log in with ‘huggingface-cli login’, then upload with ‘huggingface-cli upload <username>/<model-name> ./output’.

06RunPod Setup Details

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Your training environment under /workspace/fine-tuning/ contains: examples/ (sample configs and scripts), outputs/ (results), and config.yaml. The initial config is generated from your chosen base model and dataset. For more options and examples, see the official Axolotl examples repository. Use LoRA or QLoRA in the config to reduce GPU memory and cost when fine-tuning large models.

07Cost-Efficient Training: LoRA and QLoRA

Full fine-tuning of a 70B model can require hundreds of GB of GPU memory. LoRA (Low-Rank Adaptation) and QLoRA (quantized LoRA) train only small adapter weights, drastically cutting VRAM and cost. On RunPod you can fine-tune 7B models on a single 40GB A100 and larger models on 80GB or multi-GPU setups. Choose the smallest GPU that fits your model and batch size to keep costs down.

08Other RunPod Options

Besides dedicated Fine Tuning Pods, RunPod offers: Instant Clusters for multi-node GPU clusters (e.g. Slurm) for distributed training, and Serverless for inference or smaller jobs without managing Pods. For image models (e.g. LoRAs for Stable Diffusion), you can deploy a custom Pod with Kohya_ss or similar tools and use the same RunPod GPU infrastructure.

09Quick Comparison

No-code (Teachable Machine, etc.): Easiest start, no GPU, limited to small classifiers. Local PC: Full control, one-time hardware cost; limited by your GPU for large models. RunPod (and similar clouds): Pay-per-use GPUs, scale to large LLMs and multi-GPU; you manage config and scripts. For most “train my own AI” goals, start with no-code or local LoRA training; move to RunPod when you need more VRAM or distributed training.

Beginner / no code: Teachable Machine or ML for Kids.
LLM fine-tuning: RunPod + Axolotl + LoRA/QLoRA.
Image LoRAs: Local with Kohya_ss or RunPod Pod with the same stack.