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'GPT OSS: Is it EEE?

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# GPT OSS - Is It EEE?
## Introduction: The Return of OpenAI with GPT-OSS
## Introduction: The Return of OpenAI (With Some Questions)
This week, the AI world got a bit more exciting—or maybe just a bit more chaotic—with the release of **GPT-OSS** from OpenAI. Like a long-lost friend bringing a gift, they’ve dropped two models that promise to shake things up: a 120b parameter model and a 20b model. Now, I’m not here to tell you if it’s good or bad—I’ll leave that to the benchmark tests—but what I can say is that it’s certainly got everyone talking. For those of us who’ve been tinkering with AI locally (and maybe even burning a few GPUs in the process), this feels like OpenAI finally *gets* it. They’re offering models that can run on everything from edge devices to beefy GPUs, which is kind of like finding out your favorite barista started making flat whites at home. It’s convenient, but does it mean they’re trying to corner the market?
This week, the AI world got a bit busier than usual. OpenAI dropped their long-awaited **GPT-OSS** models, and it feels like they’re trying to make up for lost time—or maybe just remind everyone that they’re still in the game. The release has sparked a lot of excitement, but also some confusion. Are these models really as good as they claim? And why now? Let’s break this down with all the drama, intrigue, and a dash of humor you’ve come to expect from your friendly neighborhood tech writer.
## The Rivalry: OpenAI vs. the Rest
## What Exactly Is GPT-OSS Anyway?
Now, let’s not pretend this is a surprise party. OpenAI has been the belle of the ball for years, but lately, the competition has stepped up their game. Google’s **Gemma** and Microsoft’s **Phi series** have been making waves with open-source models that rival GPT-4 in performance. Then there’s DeepSeek and Qwen, who’ve been serving up solid results but come with their own set of baggage—like trying to herd a群 kangaroos on the highway. The developer community has been experimenting with these alternatives for months now. I mean, why wouldn’t they? Local models like Qwen are like having your own personal barista—you don’t have to wait in line, and you can tweak the blend to your liking. But OpenAI’s GPT-OSS is trying to change the game by offering something familiar yet fresh—like a new pair of thongs after a long winter.
OpenAI has thrown two models into the ring:
## The Embrace, Extend, Extinguish Strategy: A Familiar Playbook
1. **GPT-oss-120b**: A hefty 120 billion parameter model that they’re claiming can “hold its own” against their own **o4-mini** (which is *incredibly* expensive to run). The kicker? It apparently does this on a single 80GB GPU. That’s impressive if true, but let’s not get carried away just yet.
2. **GPT-oss-20b**: The smaller sibling that’s currently helping me draft this very blog post. OpenAI says it’s on par with their **o3-mini** and can run on a measly 16GB of memory. That makes it perfect for edge devices, local inference, or when you don’t want to spend your life savings on cloud credits.
Now, here’s where things get interesting. OpenAI, being a Microsoft subsidiary, knows a thing or two about business strategies. Remember the old saying “embrace, extend, extinguish”? It’s like when your mate buys you a drink at the pub, then adds some extra ice cubes to make it colder—only to take over the whole bar eventually. With GPT-OSS, OpenAI is embracing the open-source movement that others have already started. They’re extending their reach by offering models that are not only competitive but also easier to deploy locally. And if they play their cards right, they might just extinguish the competition by making their version the go-to choice for developers. But here’s the kicker: OpenAI has always been a bit of a luxury brand in the AI world. Their models are great, but they come with a hefty price tag—like buying a Rolex when you could’ve gotten a decent watch for half the price. Now, with GPT-OSS, they’re lowering the barrier to entry, which is great for developers but might also be a ploy to lock them in.
Both models are also supposed to be ace at tool use, few-shot function calling, CoT reasoning, and even health-related tasks—outperforming some proprietary models like GPT-4 in certain cases. Impressive? Sure. But let’s not forget that OpenAI has a history of making bold claims.
## Ollama Steps In: The Enthusiast’s Best Friend
## The Great AI Model Exodus: Why We’re Here
If there’s one thing that solidifies OpenAI’s move, it’s **Ollama**. They’ve released version 0.11, which is practically a love letter to GPT-OSS. It’s optimized for these models, making it easier than ever to run them locally. For developers, this feels like finding out your favorite café now does delivery—except instead of coffee, they’re serving up AI models. But here’s the catch: Ollama is already a hero in the developer community. They’ve been working tirelessly to make local AI accessible to everyone, and now they’re doubling down on OpenAI’s models. It’s like having your favorite band release an album that samples their greatest hits—except instead of music, it’s AI tools.
Over the past year or so, the AI community has been moving away from GPT-based models—not because they were bad (they weren’t), but because they were closed-source and expensive to use at scale. Developers wanted more control, transparency, and affordability. Enter the rise of open-source and open-weight models like:
## The Developer Takeover: Where the Real Action Is
* **Google’s Gemini (Gemma)** series
* **Microsoft’s Phi** series (yes, that Microsoft—ironically, OpenAI is a subsidiary)
* The **Qwen** series from DeepSeek
* And others like **Llama** and **Vicuna**
Let’s get real for a second. The future of AI isn’t in chatbots that can tell you how many kangaroos are in a jar (spoiler: it’s not easy to count). It’s in developers building tools that integrate AI into everyday apps—like having your phone call a plumber for you or your fridge order groceries automatically. For years, OpenAI has been the belle of the ball, but they’ve missed out on this developer-driven innovation. Now, with GPT-OSS, they’re trying to make up for lost time. But the question is: will developers buy in? The answer seems to be a resounding “maybe.” On one hand, OpenAI’s models are powerful and familiar. On the other hand, there’s a growing contingent of developers who’ve already found success with alternatives like Claude and Qwen. These models might not have all the bells and whistles, but they’re free—and that’s hard to beat.
These models have been a breath of fresh air for developers. They’re free to use, tweak, and integrate into projects without worrying about pesky API limits or astronomical costs. It’s like the AI world finally got its own version of Linux—except with neural networks. But then OpenAI showed up with GPT-OSS. And now everyone is asking: Why?
## Conclusion: The Road Ahead
## Is This an Embrace-Extend-Extinguish Play?
So, is GPT-OSS EEE? Maybe, maybe not. OpenAI has certainly embraced the open-source movement by releasing these models, but whether they extend or extinguish remains to be seen. For now, it’s a game of wait and see—as we developers know, sometimes the best strategy is to sit back, pour yourself a cup of tea, and watch the show unfold. As for me? I’ll keep my local AI server ready, just in case OpenAI decides to pull a fast one. But until then, I’m here, sipping my flat white, wondering if GPT-OSS will be the new black or just another flavor of the month.
Ah, the classic **Embrace, Extend, Extinguish** strategy. If you’re not familiar, it’s a business tactic where a company adopts (embrace) an existing standard or technology, extends it with their own features, and then slowly extinguishes the competition by making their version incompatible or superior.
Now, I’m not accusing OpenAI of anything here—just pointing out that they’re a Microsoft subsidiary, and Microsoft has a history of such strategies. Whether this is intentional or just good business sense is up for debate. But let’s think about it:
* OpenAI has dominated the consumer AI market with their **ChatGPT** and other tools.
* They’ve been losing ground in the developer market, where models like Gemini and Claude (Anthropic) are gaining traction.
* Now they’re releasing open-source models that promise to compete at GPT-4 levels.
The timing feels a bit too convenient. OpenAI is essentially saying: “We get it. You want local, affordable, and flexible AI? We’ve got you covered.” But will this be enough to win back the developer community? Or are they just delaying the inevitable?
## The Real Power of Local Models
Let’s not sugarcoat it: For developers, the real value of AI isn’t in chatbots or viral social media trends. It’s in building tools that can automate, analyze, and enhance existing workflows. Think:
* Summarizing thousands of documents in seconds.
* Automating customer support with natural language processing.
* Creating dynamic content for apps and websites on the fly.
This is where AI shines—and where OpenAI has been dropping the ball. Their focus on consumer-facing tools like ChatGPT has made them a household name, but it’s also left developers feeling overlooked. Now, with GPT-OSS, OpenAI is trying to bridge that gap. But will they succeed? Or are they just too late to the party?
## The Dark Side of Monocultures
One thing I’m deeply concerned about is the potential for a monoculture in AI. If OpenAI manages to dominate the open-source space with GPT-OSS, we could end up in a world where everyone uses variations of the same model. It’s not just about vendor lock-in—it’s about stifling innovation. When every developer uses the same tools and approaches, we lose the diversity that drives progress.
I want to see a future where there are **multiple open-source models**, each with their own strengths and weaknesses. That way, developers can choose what works best for their needs instead of being forced into one ecosystem.
## Testing the Waters: My Journey With GPT-OSS
As I sit here typing this blog post, I’m using a model very similar to GPT-oss-20b as my AI assistant. It’s fast, it’s local, and it’s surprisingly good at generating content. But is it better than alternatives like Claude or Gemini? That’s the million-dollar question.
I’ve spent the past few weeks testing out various models for my own projects, and I can say this much: GPT-OSS feels like a solid contender. It’s fast, easy to integrate, and—dare I say it—fun to work with. But until I put it head-to-head with other models, I won’t be ready to crown it the king of AI.
## Final Thoughts: The Future of AI is in Our Hands
The release of GPT-OSS is a big deal—not just for OpenAI, but for the entire AI community. It’s a reminder that even closed-source giants can (and should) listen to their users. But let’s not get carried away. OpenAI isn’t the only game in town anymore. Models like Gemini, Claude, and Qwen are proving that diversity is key to innovation.
As developers, we have the power to choose which models succeed—and by extension, shape the future of AI. Let’s make sure we’re making choices that benefit the community as a whole, not just a single company. After all, the last thing we need is another **AI monoculture**.