In the ever-evolving landscape of artificial intelligence, one of the biggest questions remains: how do locally run models compare to cloud-based, machine-fed ones? As AI technology becomes more accessible, enthusiasts and professionals alike are experimenting with models that can be hosted entirely on personal machines rather than relying on the computational power of cloud providers. But does local AI hold up against industry giants like OpenAI’s GPT-4o? To explore this question, I conducted an experiment using two distinct AI-generated responses.

The Controlled Test: Understanding Neural Networks

For the first test, I provided the same prompt to both a local AI model (DeepSeek R1) and GPT-4o. The task was simple:

Prompt: “Explain in simple terms how a neural network learns and improves over time in two paragraphs.”

GPT-4o’s Response:

GPT-4o delivered a structured and concise answer, explaining neural networks using an easy-to-understand analogy. It focused on weight adjustments and backpropagation, likening the process to learning how to shoot a basketball, refining the skill over time with practice. The response was direct, educational, and engaging, making it easy for a layperson to grasp.

A neural network learns by adjusting its internal connections (called weights) based on the data it sees. It starts with random weights and makes guesses about the correct answers. Each guess is compared to the actual answer, and the difference (error) is measured. The network then tweaks its weights slightly to reduce this error using a process called backpropagation, which is like figuring out which mistakes were made and adjusting accordingly. This process repeats many times, allowing the network to gradually get better at recognizing patterns and making accurate predictions.

Over time, as the neural network sees more data and continuously updates its weights, it improves its ability to generalize—meaning it can make good predictions even on new, unseen data. Think of it like learning to shoot a basketball: at first, you miss a lot, but by adjusting your aim based on past shots, you get better. Eventually, after enough practice, the network (or you) becomes skilled at making the right choices without needing to consciously think about every step.

DeepSeek R1’s Response:

DeepSeek R1, on the other hand, provided a more technical answer. While it also explained weight adjustments and generalization, it introduced additional concepts like dropout layers, hyperparameter tuning, and overfitting prevention. The response was more detailed but slightly less approachable for non-technical readers. Additionally, the model ignored the two-paragraph limit and instead generated a lengthier explanation.

A neural network learns through a process that mirrors the way the human brain processes information. It consists of layers of interconnected nodes, known as neurons, which work together to recognize patterns in data. When trained on a dataset, such as images labeled for classification, the network adjusts its internal parameters (weights) to minimize errors between its predictions and the actual outcomes. This adjustment happens through a process called backpropagation, where the network calculates how much each weight contributed to incorrect predictions and makes small corrections accordingly.

Over time, the network improves by generalizing from the training data, meaning it learns not just specific examples but also underlying patterns that allow it to make accurate predictions on new, unseen data. Techniques like dropout layers or regularization are employed to prevent overfitting, ensuring the network doesn’t memorize the training data and can apply its learning effectively in real-world scenarios.

The learning process is iterative. The network processes input data through multiple layers, adjusting weights based on errors using algorithms like gradient descent. Each cycle of processing is called an epoch, with many epochs allowing the network to refine its parameters continuously. By leveraging vast datasets and optimizing hyperparameters, the neural network gradually enhances its performance, becoming more accurate as it “learns” from the data over time.

Observations from the First Test:

1. Processing Time: The local model took significantly longer to generate its response compared to GPT-4o, highlighting the efficiency advantage of cloud-based AI.
2. Instruction Adherence: GPT-4o strictly followed the prompt’s instruction of a two-paragraph answer, while the local model exceeded the length requirement.
3. Readability vs. Technical Depth: GPT-4o focused on clear, digestible information, while DeepSeek R1 leaned into a more technical explanation, assuming a higher baseline knowledge from the reader.

While both responses had their strengths, it was clear that GPT-4o optimized for accessibility and clarity, whereas DeepSeek R1 prioritized technical comprehensiveness, even if it meant disregarding the length constraint.

The Blind Test: Supernatural Detective Story

For the second test, I crafted a different challenge—a short story prompt. This time, I will not reveal which response came from which model. Below, you will find two AI-generated short stories based on the following prompt:

“Write a short story about a detective investigating a crime that turns out to have an unexpected, supernatural twist. Limit your response to five paragraphs.”

Both responses have been presented as-is, without any modifications, and now, it’s up to you! Can you tell which response came from the local model and which one was generated by GPT-4o?

Option 1 –

Option 2 –

Detective Inspector Mr. Thompson had spent a good portion of his evening poring over files in his study, his coffee cup half-empty but never truly disturbed by its absence. The case he was working on—embezzlement of a rare diamond necklace from the town’s historic clock tower—wasn’t particularly unusual at first glance. The victim, an elderly man with a history of financial misconduct, had been found dead in his study earlier that morning, his body covered in strange bruises and his face marked by something he denied was a smile. As a seasoned detective, Thompson knew such details pointed toward more than a simple accident or even a straightforward burglary.

The first clue came from the victim’s office itself—a tiny, cluttered room filled with old photographs and letters. Among the finds was an old, yellowed photograph of himself taken in his late thirties, looking directly into the camera as if reminiscing about a time he’d forgotten. The photo was accompanied by a note scrawled in handwriting that Thompson could barely make out—“This is my ‘boy friend.’ He’s not coming back.”

As Thompson sifted through the evidence, he noticed something peculiar about the victim’s desk—a hidden compartment in one of the drawers contained a small, ornate box. When he opened it, his breath caught. Inside was an unopened letter addressed to “Herbert.” The handwriting was flawless—elegant and precise, as if someone who had written it many times before. The contents were even more unsettling: a map of the town, with arrows pointing toward several key locations, including the clock tower.

That night, while reflecting on these strange discoveries, Thompson couldn’t shake the feeling that something supernatural was at play. It was an inkling he knew well how to follow—his instincts had led him to similar conclusions before, often resulting in dangerous consequences. But this time, his curiosity and loyalty drove him forward, even as he struggled with the moral tension of pursuing leads that could—or should—have ended his career.

The next morning, Thompson turned up the street at a random corner, his cane tapping against the cobblestones. The shadowy figure he’d seen earlier was still lingering near the clock tower, its form shifting slightly in the half-light. “Good morning,” Thompson called out, but there was no response—or rather, there was only the faintest whisper of something moving behind the doors of the tower.

As he approached, he noticed a small window on the second floor. He reached out to touch it and gasped when he felt a strange pull—a tiny draft that made him shiver, as if unseen forces were at work within the building itself. “This is… something,” he muttered under his breath, stepping closer.

Inside, he found a dimly lit room filled with antiques—more evidence of someone who had lived a long and eventful life. Among the furniture was an ornate, polished mahogany chest. Thompson hesitated for a moment before opening it. Inside lay the letter—the one written in “Herbert’s” handwriting—and a single key hidden in a drawer. It was as if the writer had known he would find it.

With this evidence, Thompson felt a surge of determination. He knew his next move would be fraught with danger, but it was also clear that this case could not end without uncovering its true nature. As he prepared to leave the scene, he glanced at the shadowy figure once more and nodded. He had made his decision—no matter how perilous, he intended to follow his nose until the truth emerged.

Back at his office, Thompson sat alone, staring at the letter in his hands. The handwriting was unmistakable—a perfect match for “Herbert.” But what truly shocked him was the map that accompanied it. It showed not just one or two locations—the ones mentioned earlier—but a dozen points of interest across the entire county. And among those dots were several linked to his own criminal history.

This discovery sent Thompson into a tailspin. He had always struggled with the moral gray areas—cases where loyalty to justice clashed with personal safety. But now, he couldn’t shake the feeling that following this trail would either clear him or trap him in an even darker corner of the law. As he threw on his coat and exited his office, the faint presence behind the clock tower seemed to watch him leave, its ethereal form dissapearing like smoke in the early morning mist.

It was a price Thompson was willing to pay—for justice, for closure, and—for the chance that this case might finally make sense.

Cast Your Vote

Using the poll below, submit your best guess on which model generated each story. Once the poll closes, I’ll reveal the answer and share final thoughts on this fascinating comparison between local and cloud-based AI capabilities.

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Coming Soon

Which AI do you think was the locally hosted machine?

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This experiment sheds light on the strengths and weaknesses of both AI approaches. Cloud-based models like GPT-4o benefit from extensive training data, vast computational power, and strict adherence to user instructions. Meanwhile, local models offer increased privacy and control but may require more tuning to refine their responses. As AI technology advances, the gap between these models will continue to narrow, leaving users with more choices on how they interact with artificial intelligence.

So, which story do you think was written by the local model? Make your choice in the poll below!