In a world obsessed with speed, we often forget the charm of older machines. But some enthusiasts refuse to let that nostalgia fade. They push boundaries. They experiment with modern software on ancient systems. They do this not out of convenience, but out of sheer curiosity. The recent news about Llama 2 running on Windows 98 exemplifies this. It’s a testament to the creative spirit of the tech community.
Llama 2, the open-source large language model from Meta, has been making waves ever since its release. People have tested it on modern PCs, Raspberry Pis, and even older laptops. Yet nobody quite expected it to appear on a Windows 98 machine. That changed when a group of modders took it upon themselves to combine the old with the new. They tinkered. They tested. They overcame limitations. In the end, they managed to get Llama 2 to run on a Windows 98 system. Astounding.
This story first appeared on Hackaday on January 13, 2025. Shortly after, PC Gamer picked it up. They showcased the modders who accomplished this unlikely feat. The articles highlighted the knowledge, patience, and sense of adventure required for such a project. Enthusiasts everywhere were enthralled. How did they do it? Could any vintage computer do the same? Would this open a door to more AI integration in unexpected places?
Today, we’ll dive into the details. We’ll parse how the modders achieved this milestone. We’ll examine the constraints that nearly stopped them. Then we’ll reflect on the broader implications. Let’s begin with a short backstory on Llama 2.
Background: Llama 2’s Emergence

Meta, formerly known as Facebook’s parent company, released Llama 2 as a large language model with an open-source approach. AI researchers, hobbyists, and developers around the globe welcomed this move. They began to experiment with Llama 2 on powerful machines, performing tasks like text generation and code completion. Llama 2 stood out for its relative ease of fine-tuning. It also earned praise for its smaller footprint compared to some other massive models.
Even so, Llama 2 is still quite large. Running it on a modern system often requires a dedicated GPU or some fancy optimization. Some people tried to shrink it further, employing techniques like quantization. They sought to cram the model into smaller memory spaces. However, nobody anticipated it showing up on older operating systems. After all, Windows 98 is more than two decades old. Its official support ended years ago. Drivers are challenging to find. Libraries are outdated. Modern toolchains rarely consider it.
That’s precisely why this project sparked so much excitement. It defied convention. It suggested that maybe, just maybe, we can push older machines beyond their assumed limits.
Why Windows 98?
Windows 98 was a beloved operating system. It improved upon Windows 95 with better USB support and a slightly more stable environment. Yet, by modern standards, it’s antiquated. It lacks built-in drivers for much of today’s hardware. It doesn’t naturally support advanced instruction sets. And it certainly wasn’t designed with AI or GPU-accelerated computing in mind.
So why bother? For the modders, the question is reversed: Why not? Vintage computing is popular with enthusiasts who yearn for the simplicity of older software. They enjoy the challenge of making new tech work in a retro environment. It’s akin to mechanical watch aficionados who appreciate fine, older timepieces. There’s beauty in constraints. That doesn’t mean it was easy to get Llama 2 running here. Far from it.
The project demanded specialized knowledge. The modders had to overcome driver compatibility issues, memory limitations, and archaic CPU architectures. They studied older compilers and made them speak the language of new code. This was no trivial exercise. It was a marathon of trial and error.
Initial Inspiration and Goals

According to the Hackaday article, the seeds for this experiment were planted in a retro computing forum. A user joked about running “futuristic AI” on a vintage machine. Another user took that as a dare. Before long, a group formed around the idea. Their goal: prove that Llama 2 could run—albeit slowly—on a 1990s operating system.
They set some benchmarks. They weren’t expecting blazing performance. They just wanted the model to load, process prompts, and return results without crashing. Even a slow trickle of text was considered success. They had no illusions about matching a modern GPU’s throughput. They simply wanted to keep the spirit of exploration alive.
They also had something to prove. Retro computing often gets dismissed as a hobby with no real-world utility. But these modders believed that bridging the gap between old and new could yield some fascinating insights. Perhaps it could inspire more efficient software design. Perhaps it could highlight the resilience of older systems. Perhaps it could even pave the way for educational experiments.
Technical Hurdles
Running Llama 2 demands certain resources. It’s typically compiled to take advantage of modern CPU instructions. For instance, AVX or AVX2 instructions, which older processors lack. So the modders had to rewrite or modify parts of the code to avoid those advanced instructions. They leveraged retro-friendly compilers. They experimented with alternative libraries. They painstakingly replaced function calls to ensure they’d work on an ancient kernel.
Memory was another hurdle. Llama 2 is large. Even the smaller quantized versions are hundreds of megabytes in size. Windows 98 can only comfortably handle so much. If your hardware has limited RAM, it’s easy to exceed available capacity. The modders used a machine with an upgraded motherboard, sporting the maximum memory that Windows 98 could manage reliably. They also tested swap files and caching methods. In the end, they squeezed the model into a fraction of its original size. That took creativity.
Drivers posed yet another challenge. Modern GPUs were out of the question. The group had to rely on older video cards or integrated solutions. They even tested some forms of GPU acceleration using obscure drivers, but success was minimal. Ultimately, they concluded that CPU-only inference was the best bet. This made the processing painfully slow. Still, it worked, at least to the extent that text output was eventually produced.
Even network connectivity was an issue. Windows 98 can use the internet, but security holes abound. The modders had to patch and protect the system carefully. They also installed the right networking protocols to talk to the rest of the local network. Some folks might question why they’d bother hooking Windows 98 to the internet in 2025. The answer is that it allowed them to remotely monitor the system, send commands, and retrieve logs.
Surprising Success
After weeks of trial and error, the modders reported success. The initial demonstration was modest. They typed a prompt into a retro terminal window. Then they waited. And waited some more. Eventually, Llama 2 responded with a coherent sentence. The crowd on the forum erupted in virtual applause. It was slow. It was unglamorous. But it was a marvel all the same.
Once word got out, it spread quickly. The Hackaday piece highlighted the achievement in a detailed post. Readers marveled at the screenshots: an old CRT monitor displaying a DOS-like prompt, with Llama 2 spitting out text. Nostalgia blended with admiration. Some users joked about building AI chatbots in Windows 3.1 next. Others speculated on what else might be possible.
Then PC Gamer covered the story. They described the group’s process in broad strokes. They also talked about the significance of bridging modern AI with legacy systems. They stressed that it wasn’t purely a “fun” project. It showed how resourceful hobbyists can keep old tech alive. It also hinted at potential educational uses: demonstrating the fundamentals of AI on less abstracted platforms.
Some naysayers questioned the practicality. Why spend so much time and effort just to run a slow chatbot on an ancient machine? The modders replied that this question missed the point. They see it as a labor of love, a puzzle to solve, and a reminder that technology doesn’t have to be ephemeral.
Performance Metrics
To manage expectations, the modders shared some performance metrics. Llama 2 on Windows 98 was glacially slow by modern standards. The system took minutes to generate a single sentence. It sometimes required manual restarts. The system’s CPU usage spiked to 100% for prolonged periods. The hard drive churned. The fans whirred. But it never fully crashed.
Users looking for actual day-to-day AI assistance might find this setup impractical. But for demonstration purposes, it worked. The sense of accomplishment overshadowed performance concerns. The group also noted that with further optimization, they might squeeze out slightly faster speeds. They’re exploring older CPU instruction sets in greater detail. They’re also considering ways to break the model into smaller parts. They’re not done tinkering yet.
Ironically, the limitations of Windows 98 forced them to code in more efficient ways. They had to watch memory usage closely. They had to reduce overhead. In a sense, that skill—optimizing code for minimal resources—has become rarer in an age of abundant computing power. In that way, the project served as a valuable learning experience.
Broader Implications
Does this achievement mean everyone should rush to install AI chatbots on vintage PCs? Probably not. Still, there are some intriguing implications:
- Efficiency: One of the biggest complaints about modern AI is the resource drain. Projects like this highlight the possibility of scaling down. If a large model can limp along in Windows 98, it might run with less overhead than we think on more modern systems. That’s encouraging for low-power devices or edge computing.
- Longevity of Hardware: We often discard old PCs. But this project is a reminder that they can still be useful, especially for specialized tasks or experimentation. It’s a call to reconsider e-waste practices. Could we repurpose old machines for simpler AI workloads?
- Education: Running Llama 2 on Windows 98 is a masterclass in debugging, low-level optimization, and backward compatibility. Students of computer science could learn a lot from such an endeavor. They’d see how each layer of the software stack interacts. They’d also gain respect for hardware constraints.
- Community Spirit: The modding community thrives on collaboration. This project underscores the power of open forums and shared knowledge. It wasn’t a big corporation that pulled this off. It was a group of dedicated individuals fueled by curiosity.
- Security Concerns: It’s important to note that Windows 98 is not secure. If someone wanted to do serious AI tasks, connecting a Windows 98 system to the internet might be risky. The modders themselves acknowledged this. Their solution was to sandbox the system. For the rest of us, it’s a warning sign. Don’t treat a Windows 98 AI box as a daily driver.
- New Use Cases: While slow, Llama 2 on Windows 98 might be enough for certain offline tasks. People who enjoy old-school computing might appreciate having a local language model for creative writing or small coding suggestions—assuming they can handle the wait times.
These implications illustrate why this project resonates beyond a small group of retro hobbyists. It’s a conversation starter. Where else could we push modern AI in improbable directions?
The Tinkerers Behind the Scenes
PC Gamer interviewed some of the key players. They described themselves as “a collective of tech geeks who grew up in the ‘90s.” They work remotely, meeting in a chatroom that looks suspiciously like an IRC throwback. They share code on Git repositories. They celebrate even the smallest victories, like fixing a memory leak that shaved a few seconds off the inference time.
One of the modders explained the thrill of hearing the hard drive churn as Llama 2 processed data. That tactile sense is lost in solid-state drives. Another mentioned the delight in seeing the Windows 98 startup screen, followed by a command window and lines of AI-generated text. It was surreal, they said, like watching time travel in action.
They had help from a global network. People chipped in with code patches, driver links, or even moral support. Some tested the builds on their own machines. Others donated parts like old graphics cards, which might or might not help in accelerating the AI. Collaboration was key. Without a robust and enthusiastic community, the project could have easily stalled.
They also emphasized safety. They urged anyone following their footsteps to isolate their Windows 98 setups from critical networks. They recommended frequent backups. They recognized the risk that the OS might implode under the stress. Indeed, their test machine crashed more than once. They learned to keep multiple backups of drivers and configuration files.
Could It Go Further?
What if you tried Windows 95? Or DOS? Enthusiasts love these hypothetical questions. In theory, more minimal systems could be used. But you’d have to strip down the AI model even further, or rely on advanced memory management tactics. That’s another rabbit hole. Right now, Windows 98 seems like a sweet spot of vintage charm and enough modern capability to do the job.
Some folks also wonder if they can integrate a GPU from the early 2000s. That might speed up the process—slightly. Others are curious about distributing the load across multiple vintage machines. A “Windows 98 AI cluster” is a hilarious mental image, but it could be possible. Could you chain together the resources of multiple old PCs to process Llama 2’s computations faster? Possibly. But the overhead might be enormous.
For now, the modders are content with their Windows 98 success. They plan to release a detailed tutorial. It will walk intrepid users through the steps. From installing Windows 98 on modern drives (which might require adapters) to patching the OS. From customizing compilers to carefully adjusting memory settings. They hope to make it easier for others to replicate.
Retro Computing Renaissance
Projects like these aren’t entirely new. Retro computing has had a resurgence. People run Linux on 486 machines. They install older versions of BSD on ancient hardware. They bring back the Amiga with modern expansions. Even so, bridging the gap to modern AI is a leap. Large language models demand far more resources than older software. Yet this group showed that with enough persistence, you can bring them together.
The result is an odd synergy. You have a 1990s interface, complete with chunky fonts, gray toolbars, and a Start menu that’s not even round. Under the hood, a carefully stripped-down AI model is churning away, producing futuristic text. It’s a conversation that crosses decades. Every beep and whirr from the machine reminds you of the past. Every generated sentence hints at the future.
There’s something poetic about that. Technology doesn’t have to be a straight line from old to new, discarding everything that came before. Sometimes it can loop back and create surprising connections. That spirit resonates with a niche but passionate community.
The Learning Aspect
Aside from novelty, this project offers valuable lessons:
- Backward Compatibility Matters: When modern code fails on older systems, it highlights how quickly software moves on. But it also reveals which features are truly essential. Maybe we take advanced instruction sets for granted, ignoring those who rely on older hardware.
- Optimizing Code: Developers have grown accustomed to large memory spaces. Running Llama 2 on Windows 98 forces a shift in perspective. Memory is precious. CPU cycles are finite. Every line of code matters. That’s a lost art in some circles, but it could make modern software better if we pay attention.
- Community Knowledge Sharing: This project wouldn’t have happened without a global group of enthusiasts. They leaned on each other’s expertise. They documented everything. That’s the open-source spirit. It’s also a reminder that sometimes the biggest breakthroughs come from volunteer collaboration.
- Curiosity as a Catalyst: Curiosity drove the entire effort. Nobody got paid to do this. They just wanted to see if it was possible. That raw sense of wonder remains a powerful motivator in tech. It leads to discoveries that no business plan might predict.
- Technical Endurance: Old systems can still serve specialized purposes. They might not be fast or secure enough for daily tasks. But for learning, experimentation, or even digital art projects, they can shine in unexpected ways.
These insights push us to reconsider our assumptions. The moment we see an older system, we often think it’s obsolete. This story shows that “obsolete” is a relative term. With enough dedication and imagination, you can coax advanced software into that environment.
Windows 98: The New Frontier for AI?

Is this the start of a trend? Probably not in a mainstream sense. Yet it’s entirely possible we’ll see more experiments. Another group might try to run a smaller generative model on Windows 95. Someone else might attempt speech recognition on a 486. The retro computing community is full of surprises. Once they see it can be done, they rarely let it rest.
We might also see a wave of tutorials, videos, and blog posts capturing this effort. Imagine a YouTube series called “AI on Ancient OSes.” Each episode explores a different era. Maybe there’s a comedic spin. Or maybe it’s geared toward hardcore enthusiasts who want to replicate the experience at home. One thing is certain: the excitement is real.
Reflecting on the Journey
It’s easy to dismiss these achievements as mere stunts. Running Llama 2 on a Windows 98 machine that takes minutes to produce a few words might seem pointless in a world where advanced GPUs can generate entire paragraphs in seconds. But that standpoint misses the deeper story. This is about pushing boundaries. It’s about celebrating human ingenuity. It’s about preserving a little bit of computing history while connecting it to the present day.
In that sense, this project is a triumph. It shows how vibrant the modding community remains. It underscores that technology can transcend its original environment when enthusiasts apply their collective knowledge. And it offers a glimpse into a future where AI might not always need the latest hardware to function, especially if we continue to find ways to optimize and adapt.
Conclusion
Llama 2 on Windows 98 is more than a curiosity. It’s a statement about resilience, creativity, and collaboration. Yes, it’s slow. Yes, it’s outdated. And yes, it’s absolutely incredible. The modders took on a challenge that many would have dismissed outright. They overcame driver issues, compiler limitations, memory constraints, and security concerns. In return, they demonstrated that modern AI and legacy systems can coexist—even if only barely.
This project reminds us that technology’s evolution isn’t a one-way street. Sometimes, it loops back on itself. We rediscover old hardware in new contexts. We blend the past and the present. We glean insights about efficiency, code optimization, and the ephemeral nature of “cutting-edge” tools. Ultimately, that spirit of exploration is what keeps the tech world vibrant. It keeps us curious and a little bit playful. And that’s how breakthroughs happen.
So next time someone tells you Windows 98 belongs in a museum, point them to Llama 2 running on that classic OS. It might not be practical for daily AI tasks. But it’s a testament to what a few determined enthusiasts can do when they really set their minds to it. And who knows, maybe this is just the beginning of more improbable AI mashups to come.