Fine-Tuning LLaMA 3 with LoRA on DataCrunch vs DigitalOcean

When picking a cloud provider for machine learning, it’s tempting to focus on technical specs or hourly pricing. But those numbers don’t tell the whole story. What really matters is the experience: how easy it is to get started, how fast your training runs, and how much it actually costs to complete a real task.

To find out how much the platform itself matters (not just the GPU), I ran a practical benchmark comparing DataCrunch and DigitalOcean. I fine-tuned Meta’s LLaMA 3.2 3B Instruct model using LoRA adapters, on the same class of GPU: NVIDIA H100. The goal wasn’t just to see which was faster, but to understand what it’s actually like to train on each platform.

Experiment Setup

I designed the benchmark around a realistic training workload. Instead of a toy task or synthetic benchmark, I created something that reflects what you’d run as a researcher or engineer doing early-stage prototyping.

The model was Meta’s LLaMA 3.2 3B Instruct, a compact instruction-tuned language model well-suited for quick experiments. For training, I used LoRA adapters, which allow you to fine-tune specific parts of a model while leaving most of the weights frozen. This dramatically reduces memory usage and compute requirements while still allowing meaningful learning.

The dataset was a mix of four open instruction datasets: Alpaca, Dolly, OpenAssistant, and WizardLM. I sampled 1,250 examples from each to build a combined dataset of 5,000 samples. Each example was tokenized to a maximum sequence length of 2048 tokens, and the model was trained for a single epoch.

I enabled bfloat16 and TF32 precision optimizations to take full advantage of the H100 hardware. After some quick testing, I found that a batch size of 2 was the best fit for balancing memory usage and throughput without running into OOM errors.

Task Configuration Summary

Instance Specifications

Although both platforms used the same H100 GPU, the supporting hardware was a little different. Here’s a breakdown of the instance specs:

In this particular test, these differences didn’t seem to affect the outcome. But depending on your workload, CPU and RAM can play a larger role, especially for multi-GPU or data-heavy jobs.

Training Overview

The training code was built to be lightweight and practical. It focused on real-world usability while still taking advantage of H100 acceleration features. A few highlights:

• bfloat16 and TF32 were used to accelerate compute without sacrificing precision

• LoRA adapters were applied to projection and feed-forward layers of the transformer

• GPU memory usage and utilization were monitored using pynvml

Each run was tuned to finish in under 30 minutes to simulate a fast development loop. This setup helps evaluate not just raw speed, but also how smoothly the environment handles a typical ML workflow.

Platform Comparison

1. Setup and First Launch

On DataCrunch, the experience was seamless. As soon as the instance spun up, all key libraries were already installed. Even JupyterLab was preinstalled and ready to go, which made it easy to test, tweak, and monitor the training directly from the browser. Dataset and model downloads were fast, and the environment required no extra setup.

DigitalOcean was also preconfigured with all the necessary libraries, so it was ready for training from the start. However, JupyterLab wasn’t available out of the box. For those who prefer browser-based experimentation, some additional steps were needed to get up and running. Interestingly, I noticed slightly faster dataset download speeds on DigitalOcean, though it didn’t impact training time overall.

Both platforms were usable right away from the terminal. The key difference was that DataCrunch gave you that extra layer of convenience if you wanted to use notebooks without doing any manual setup.

2. Training Performance

Both platforms delivered almost identical performance. The model fine-tuned successfully in under 20 minutes on each, and the token throughput was within 1% of each other. DataCrunch came out just slightly ahead, but the difference is too small to matter in practice.

3. Cost Breakdown

This is where things really separate. The performance might be neck and neck, but the pricing is not. DataCrunch delivered the exact same outcome for 35% less cost. That’s not just a small difference, it’s significant, especially when you're running jobs frequently or scaling across many experiments.

4. Developer Experience

Both environments are perfectly capable for machine learning. If you work entirely from the terminal, you might not notice much difference at all. But if you use notebooks or want a smoother onboarding experience, DataCrunch saves you that extra bit of time and hassle.

TLDR Summary Table

Final Thoughts

If you're just looking at training speed, there isn’t much difference between DataCrunch and DigitalOcean. Both platforms ran the same code, on the same GPU, in almost the same amount of time. The results were equally strong, and both environments were stable and well-configured.

But price is where DataCrunch pulls ahead. With a lower hourly rate, it delivered the exact same result for significantly less money. When you're running a lot of experiments, or even just trying to keep costs in check during development, those savings can really add up.

Add to that the smoother setup, especially for notebook users, and you end up with a better overall experience. DataCrunch feels more focused on machine learning users, while DigitalOcean offers a more general-purpose environment that requires a bit more effort to customize.

For anyone doing regular fine-tuning or prototyping, that combination of lower cost and easier setup makes DataCrunch a great choice.