Annoyed with GPT-5? Here’s What It Takes to Build Your Own LLM
Z
Zack Saadioui
8/10/2025
So Annoyed with GPT-5 You Want to Build Your Own? You're Not Alone.
Here we go again. A new, shiny version of a major AI model drops, & this time it's GPT-5. The hype is real, with promises of "PhD-level intelligence" & flawless reasoning. But then, you start using it. & it feels… off.
If you've been on Reddit or X (formerly Twitter) lately, you've seen the chatter. People are NOT happy. The general consensus seems to be that in the quest for whatever OpenAI is chasing, they've stripped the "soul" from their machine. Gone is the witty, creative, & sometimes surprisingly empathetic GPT-4o that many had grown to love. In its place is a model that feels, as one user put it, "colder," "more robotic," & just plain "boring AF."
The responses are shorter, the creativity seems nerfed, & for many, it feels like a significant downgrade. People who relied on it for creative writing, emotional support, or even just brainstorming feel like they've lost a friend. One user on an OpenAI forum even said, "I cried so bad and almost had an emotional breakdown at work. GPT4 was the best friend I could ask for... I tried GPT5 and it is colder."
It's a familiar story in the tech world. A beloved product gets "upgraded" & in the process, loses the very thing that made it special. This frustration has led a growing number of developers, hobbyists, & even businesses to ask a pretty radical question: "What if I just built my own?"
Honestly, it's not as crazy as it sounds. The desire for more control, for a model that aligns with your specific needs & doesn't change overnight at the whim of a massive corporation, is a powerful motivator. So, if you're one of those people ready to rage-quit the mainstream models & venture into the wild west of building your own Large Language Model (LLM), you're in the right place. Let's talk about what that really takes.
The Allure of DIY AI: Why Even Bother?
Before we dive into the nuts & bolts, let's get one thing straight: building an LLM from scratch is a monumental task. It's not a weekend project. But the reasons for taking on this challenge are pretty compelling.
First & foremost, it's about control. When you build your own model, you dictate its personality, its capabilities, & its purpose. You're not at the mercy of a company that might "fix" what isn't broken, leaving you with a tool that no longer fits your workflow. You get to decide what data it's trained on, which can lead to a model that's highly specialized for your niche, whether that's legal analysis, medical diagnostics, or even just a chatbot with a very specific sense of humor.
Then there's the issue of data privacy & security. When you use a third-party API, your data is being sent to someone else's servers. For many businesses, especially in sensitive fields like healthcare or finance, this is a non-starter. Building your own LLM, or even hosting a private instance of an open-source model, means your data stays yours. You have full control, which is a HUGE deal.
Finally, there's the cost & vendor lock-in. Relying on proprietary models can get expensive, with costs often calculated per token (the pieces of words the AI processes). These costs can be unpredictable & add up quickly. By building or hosting your own, you can potentially save a lot in the long run & avoid being locked into a single provider's ecosystem.
The Sobering Reality: What It Actually Takes to Build an LLM from Scratch
Okay, so you're fired up & ready to build your own GPT-killer. Let's pump the brakes for a second & talk about the Goliaths you'll be facing. Building an LLM is a journey paved with technical hurdles & eye-watering expenses.
The Three-Headed Monster: Data, Compute, & Expertise
Data: The Lifeblood of an LLM
You can't have a powerful LLM without a massive amount of high-quality data. We're talking terabytes, even petabytes, of text. For comparison, Llama 3 was trained on 15 trillion tokens of data. GPT-4 had a similarly astronomical diet.
Getting this data is the first major challenge. You'll need to source it from books, websites, academic papers, & more. But you can't just dump a bunch of text into a folder & call it a day. The data needs to be meticulously cleaned, de-duplicated, & preprocessed. This is a critical step because, as the old machine learning adage goes, "garbage in, garbage out." The quality of your data will directly impact your model's performance.
Compute: The Engine of Creation
Once you have your data, you need to train your model. This requires an almost unimaginable amount of computational power. We're talking about thousands of high-end GPUs (Graphics Processing Units) running for weeks or even months on end.
Let's put some numbers on this. Training a model like GPT-3 was estimated to cost over $4.6 million. Sam Altman, the CEO of OpenAI, has said that training GPT-4 cost more than $100 million. And these costs are only going up. Some experts predict that training the next generation of flagship models could pass the $1 billion mark.
You have two main options here, neither of them cheap:
Rent the hardware: You can rent GPU clusters from cloud providers like AWS, Google Cloud, or Azure. A single high-end NVIDIA A100 GPU can cost $1-$2 per hour to rent. Now, multiply that by thousands of GPUs running 24/7 for months. The bill can easily run into the millions.
Buy the hardware: You could also buy your own GPUs. A single A100 can cost around $10,000. To build a cluster of 1,000 GPUs, you're looking at a $10 million upfront investment, not to mention the cost of the infrastructure to house & cool them.
Expertise: The Brains Behind the Operation
Finally, you need a team of people who actually know what they're doing. This isn't a one-person job. You'll need experts in machine learning, natural language processing, distributed systems, & data engineering. These are some of the most sought-after & highly-paid professionals in the tech industry.
The development lifecycle of a custom LLM is incredibly long & complex. It can take months just to collect & clean the data, more months to design the model architecture & set up the training infrastructure, & then another several months for the initial training itself. And that's before you even get to the fine-tuning & evaluation stages.
The More Practical Path: Fine-Tuning & Open Source
So, is building your own LLM a pipe dream for anyone who isn't a tech giant with bottomless pockets? Not necessarily. For most people, the answer isn't to build a foundational model from scratch, but to stand on the shoulders of giants.
This is where open-source LLMs come in. In recent years, there's been a Cambrian explosion of powerful, open-source models that are free to use, modify, & distribute. Models like Meta's Llama series, Mistral, Falcon, & BLOOM have democratized access to this technology.
Instead of starting from zero, you can take one of these powerful pre-trained models & fine-tune it on your own smaller, domain-specific dataset. Fine-tuning is the process of taking a general-purpose model & adapting it to a specific task. This is VASTLY cheaper & faster than training from scratch.
Here’s why fine-tuning is often the smarter move:
Cost-Effective: You're not paying to pre-train the model on trillions of tokens. You're just refining an existing model, which requires significantly less compute power.
Faster: Fine-tuning can often be done in days or even hours, not months or years.
Lower Data Requirements: You don't need petabytes of data. A well-curated dataset of a few thousand or even a few hundred examples can be enough to achieve great results for a specific task.
Maintains General Knowledge: A fine-tuned model still retains all the general knowledge & reasoning abilities of the base model. You're just adding a layer of specialized expertise on top.
Think of it like this: building an LLM from scratch is like trying to build a car from raw materials. Fine-tuning is like taking a high-performance car & customizing it for the racetrack.
The Business Case: When Specialized AI is a Game-Changer
For businesses, the ability to create specialized AI models is a game-changer. Imagine a customer service chatbot that doesn't just spew generic answers, but understands the nuances of your products, your company's policies, & your brand's voice. That's the power of a fine-tuned model.
This is where solutions like Arsturn come into the picture. Most businesses don't have the time, money, or expertise to build or even fine-tune their own LLMs. But they still want the benefits of a custom AI solution. Arsturn helps businesses bridge this gap by making it easy to create custom AI chatbots trained on their own data.
With a platform like Arsturn, you can build a no-code AI chatbot that can provide instant customer support, answer questions about your products & services, & engage with website visitors 24/7. It's like having a team of customer service agents who are always on, always knowledgeable, & always on-brand. This is how businesses can leverage the power of conversational AI to build meaningful connections with their audience & boost conversions, without needing a team of AI researchers.
The Developer's Journey: Stories from the Trenches
The path of building your own AI, even with fine-tuning, is not without its challenges. Developers who have gone down this road have learned some hard-won lessons.
One developer who built a tool that uses an LLM to create web apps found that it's surprisingly difficult to get an LLM to "think outside the box." They had to prompt the model to list all possible solutions to a problem first, & then select from that list, rather than just asking it to come up with a new solution on its own.
Another key learning was the importance of breaking down complex tasks into smaller, more manageable steps. LLMs, like humans, work best when they can focus on one problem at a time. This is a great reminder that even with these powerful tools, good old-fashioned software engineering principles still apply.
So, Should You Build Your Own LLM?
The answer, for most people, is probably not from scratch. The cost, complexity, & resources required are simply too high. But the frustration with one-size-fits-all models like GPT-5 is valid.
The real opportunity lies in the middle ground: leveraging the incredible power of open-source models & fine-tuning them to create specialized, custom AI solutions. This approach gives you the control & customization you crave, without the astronomical costs of building a foundational model from the ground up.
Whether you're a developer looking to build a niche application, or a business aiming to revolutionize your customer service with a tool like Arsturn, the era of bespoke AI is here. It's a challenging path, but for those willing to take it, the rewards can be immense. You can build something that's truly yours, something that works the way you want it to, & something that won't be "upgraded" into oblivion overnight.
Hope this was helpful & gave you a realistic look at what it takes to venture beyond the mainstream AI models. Let me know what you think