Can I fine-tune an 8B model (like Llama 3.1 8B) on a single 24GB GPU?

Yes! Using QLoRA (4-bit NF4 quantized base model) with gradient checkpointing and Paged AdamW 8-bit optimizer, Llama 3.1 8B requires only 8.5 GB of VRAM at 2048 sequence length, running comfortably on a single RTX 3090, RTX 4090, or 24GB cloud instance.

What is the difference between QLoRA and Standard LoRA?

Standard LoRA keeps the frozen base model in full 16-bit precision (requiring 2.0 GB per billion parameters for weights alone). QLoRA quantizes the frozen base weights to 4-bit NormalFloat (NF4), reducing base model memory to 0.55 GB per billion parameters while training 16-bit adapters on top with virtually zero accuracy loss.

Why does Gradient Checkpointing save so much VRAM?

During standard backpropagation, all intermediate forward activations must be stored in VRAM to compute gradients. Gradient Checkpointing stores only a subset of activations and recomputes the rest on the fly during the backward pass, reducing activation memory by over 75% at the cost of a small (20%) compute overhead.

LoRA & Fine-Tuning Compute, VRAM & Cloud Cost Estimator

Calculate VRAM requirements, GPU training hours, and cloud costs for QLoRA, LoRA, and Full fine-tuning.

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Technical Overview & Architecture

Calculate exact GPU VRAM memory allocation, training duration, and cloud rental costs for fine-tuning open LLMs (Llama 3.3, Qwen 2.5, Mistral). Supports QLoRA 4-bit, LoRA 16-bit, and Full Fine-Tuning across model scales (1B to 70B), sequence lengths, batch sizes, and generates production-ready Axolotl / HuggingFace training YAML configs 100% in-browser.

The sizing engine calculates base model parameters × precision bytes, adds adapter gradients proportional to rank (r), estimates optimizer states (Paged AdamW 8-bit vs AdamW 16-bit), factors sequence length activation scaling with gradient checkpointing, and computes total training FLOPs against GPU hardware TFLOPS.

Key Capabilities & Features

Granular VRAM Decomposition: Breaks down GPU memory across Base Weights, LoRA Adapter Weights/Gradients, Optimizer States, Activations, and CUDA Overhead.
Multi-Architecture Sizing (1B to 70B): Simulates memory scaling from edge models (Llama 3.2 3B) to massive flagships (Llama 3.3 70B) across QLoRA, LoRA, and Full tuning.
GPU Cloud Hardware & Cost Projection: Calculates training hours and rental costs across RTX 4090, A100 (80GB), and 8x H100 clusters with lowest-cost recommendations.
Axolotl & HuggingFace YAML Exporter: Export optimized training configuration YAML files ready for immediate execution in cloud training pipelines.

Step-by-Step Usage Guide

Select Model Size & Tuning Method: Choose parameter scale (1B to 70B) and select QLoRA 4-bit, LoRA 16-bit, or Full Fine-Tuning.
Configure LoRA Rank & Sequence Length: Set rank (r=16 to 128), target modules, sequence length, and batch size.
Analyze VRAM & Export Training Config: Review total VRAM, inspect estimated GPU cloud cost, and copy the ready Axolotl YAML file.

Frequently Asked Questions (FAQ)

Can I fine-tune an 8B model (like Llama 3.1 8B) on a single 24GB GPU?: Yes! Using QLoRA (4-bit NF4 quantized base model) with gradient checkpointing and Paged AdamW 8-bit optimizer, Llama 3.1 8B requires only ~8.5 GB of VRAM at 2048 sequence length, running comfortably on a single RTX 3090, RTX 4090, or 24GB cloud instance.
What is the difference between QLoRA and Standard LoRA?: Standard LoRA keeps the frozen base model in full 16-bit precision (requiring 2.0 GB per billion parameters for weights alone). QLoRA quantizes the frozen base weights to 4-bit NormalFloat (NF4), reducing base model memory to ~0.55 GB per billion parameters while training 16-bit adapters on top with virtually zero accuracy loss.
Why does Gradient Checkpointing save so much VRAM?: During standard backpropagation, all intermediate forward activations must be stored in VRAM to compute gradients. Gradient Checkpointing stores only a subset of activations and recomputes the rest on the fly during the backward pass, reducing activation memory by over 75% at the cost of a small (~20%) compute overhead.