Introduction: Small Models, Big Claims
Lately, I’ve been noticing a lot of buzz around small language models. YouTube thumbnails screaming “Future is Small Models!” and articles claiming “Run ChatGPT-like AI on your laptop!” It got me curious. As someone who’s into tech and development, I thought—why not give it a shot myself?
Installing Ollama and First Impressions
So, I came across two tools that seemed pretty beginner-friendly: Ollama and LM Studio. These allow you to download and run various open-source LLMs locally. I started with Ollama.
Installing it was simple enough. Download, install, and done. Next, I pulled in a few models to try chatting with. Here's the first surprise: even so-called "small models" are huge. I was expecting something like 500MB tops, but nope—some models were 3GB, 5GB, even 8GB+. That’s not small in the traditional sense.
“Hi” Prompt and Sudden Resource Spike
Anyway, I waited it out, got the models downloaded, and ran the script to start chatting. The terminal fired up and prompted me to enter my message. Just to test, I typed a simple “hi.”
And then—whoosh—my laptop fans kicked into overdrive. My CPU usage shot up to 90–95%, RAM got sucked into a black hole, and the SSD was constantly buzzing. All this for a single word? It felt like I had launched a AAA video game, not a terminal-based chat app.
Sure, I got the response back, and it worked fine for a basic exchange. But that resource usage really shocked me.
The 500-Word Story Test
Then came the real test: generating a 500-word story. Just for fun, I asked the model to write a short piece of fiction. That's when the system just gave up.
The whole laptop froze. The mouse cursor was lagging, everything was slow, and I couldn’t even switch tabs properly. My machine felt like it had aged 10 years in 10 minutes.
Now, to be clear, my system isn’t that weak. I’ve got an Intel i5, 16GB RAM, and 250GB SSD. It's not a gaming rig, but definitely not a potato either. Still, these “small” models made it feel ancient.
Running in Docker with Limits
I thought, okay—what if I run it in Docker and limit the resources? I capped CPU and RAM usage to make it more manageable. It worked, technically, but the performance dropped heavily.
The responses came, but at a snail’s pace. It took over a minute to generate a paragraph. Not practical at all.
Conclusion: CPU Isn’t Ready Yet
That’s when the reality hit me. These small models aren’t really “small” in terms of what your system needs to run them efficiently. They’re small relative to the massive cloud-based models like GPT-4. But to a regular home setup, they still demand a lot—especially if you want fast response times and usable performance.
So here’s what I concluded: unless you have a GPU, trying to run these models locally isn’t worth the effort—as of 2025. Even if you manage to run them, the heat, power consumption, and lag make it inefficient for regular use.
What I Recommend Instead
If you're thinking about using this tech for actual productivity—like writing content, coding help, or automation—then local CPU-based inference will slow you down more than it helps.
Instead, I highly recommend using cloud VMs with GPUs or services that handle the backend for you. Platforms like Replicate, Hugging Face Spaces, or even renting GPU time on RunPod or Paperspace make a lot more sense. You pay a bit, sure, but it saves your time, sanity, and hardware.
Small models are exciting, and yes—the future might be “local-first” someday. But for now, if you really want to use LLMs in a meaningful way, stick with the cloud or go GPU.
That’s my real-world take. I tried, I tested, and now I know better.
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