MythBusting LLMs: From GPU-rich Dreams to GPT-4's Gleam!

Hey there!

Lately, I've been hopping on more Zoom calls with investors and VCs than I'd like to admit. No, it's not my secret plan to start a virtual karaoke business. (Although, if you're interested, hit me up!). I've been discussing the latest tech craze—Large Language Models, or LLMs, emphasising Generative AI products.

While I've had many enlightening conversations, I've also encountered widespread misconceptions that made me want to channel my inner MythBuster. Let's dive into them!

Myth #1: Any startup can build its own GPT-4 equivalent!

Oh, where do I start? I've seen eager entrepreneurs with visions of creating a product that could transform a casual chat into a full-blown TED Talk proposal. The ambition is fantastic! But here's the glitch: even if you had all the world's data at your fingertips and money to burn on high-end computing power, would you really come close to GPT-4? To put it into perspective, creating GPT-4 wasn't just about data; it was about oodles of it! Not to mention the time and computing resources behind it. So, before you go down this path, remember the golden rule: it's not about how many GPUs you have; it's about how you use them.

Myth #2: Building our own LLM will be our competitive edge!

You do decide to venture into the LLM world. How long do you think it'll take to catch up? And even if you do catch up, is your model genuinely better than the likes of GPT-4? Plus, the time invested in such a feat! Couldn't it be better spent on refining UX or expanding your user base? It's like trying to bake a cake from scratch when there's a world-class bakery next door.

Let's sprinkle some truth seasoning on this myth. While LLMs like GPT-4 are impressive in their generality, they're like Swiss Army knives – versatile, but sometimes you need a more specific tool.

If you want your model to serve a niche or specialized purpose, simply having an LLM won't give you that razor-sharp edge. That's where prompt engineering and fine-tuning enter the scene. By tweaking and tailoring, you can mould an LLM to fit your unique product life cycle and bring that special flavour to your application.

Are you curious about the magic of fine-tuning and prompt engineering? Dive deeper into our blog post titled "A Beginner's Guide to Fine-Tuning Large Language Models" for all the juicy details.

Oh, and did we mention chain agents? They're the unsung heroes behind making models like GPT work in tandem for enhanced tasks. Imagine a relay race but with algorithms passing the baton.

To sum it up, Building an LLM is cool, but mastering its tuning and understanding chain agents? Now, that's the real secret sauce.

Myth #3: GPUs - The More, The Merrier!

I recently stumbled upon a gem from semianalysis discussing the GPU-rich and GPU-poor divide. Imagine a world where bragging about GPUs is the new flex, almost like comparing who has the bigger... yacht. But here's the harsh reality—many startups and researchers are GPU-poor. They're trying to fine-tune models without the necessary resources, which is akin to attempting a marathon in flip-flops.

This bimodal distribution of resources isn't just a quirky observation. It's shaping our industry, with titans like Nvidia establishing dominance. And, for the record, being GPU-poor isn't just a startup problem. Some big-name players in the AI space are feeling the crunch, struggling to meet the demand and competition.

Myth #4: If OpenAI becomes expensive, we're doomed!

This is a genuine concern, especially with the evolving nature of API costs. But here's the silver lining: the LLM ecosystem has the GPU-rich players leading the charge. Their vast computational resources will undoubtedly drive innovations, giving us even more tools and possibilities through APIs.

Here's a whimsical thought - if the price of your favourite chocolate doubled, would you try to grow cocoa in your backyard? Given their superior computing capabilities, GPU-rich entities will always have an edge. Their infrastructural dominance paves the way for endless innovations atop these machines via the API

OpenAI is an Insanely Good Deal

In light of these myths, we deeply explored a topic I feel passionately about. Next week's article is titled "OpenAI vs. Self-Hosted LLMs: A Cost Analysis". We put our product, SpeakerScribe, under the microscope to see if we'd benefit from a self-hosted LLM. Spoiler: We probably won’t, and it will be a long time before we do! And, for those scratching their heads over LLM costs, we've created the GPT Subscription Advisor app. It's like having a financial advisor for your LLM needs—minus the suit and tie.

In closing, while myths can be fun (I'm still looking for that pot of gold at the end of the rainbow), separating fact from fiction regarding business and technology is crucial. So, the next time you hear someone say, "Why don't we just build our own GPT?" you'll know just how to MythBust them!

Happy innovating! ??