When a “Small” AI Model Pushes Your Hardware to Its Limits

#ai #machinelearning #learning #performance

While building my ConversaAI web app, I started experimenting with a local AI model using Ollama, running the 𝗚𝗲𝗺𝗺𝗮 𝟯 (𝟭𝗕) model, a “lightweight” 𝟴𝟭𝟱 𝗠𝗕 model.
According to the documentation, a system with 8 GB RAM can handle models up to 7B parameters, so I expected a smooth run since my system has:
• CPU: Intel i7-8650U @ 1.9 GHz
• RAM: 16 GB
• OS: Windows 11 Pro
• GPU: NVIDIA MX130

But in practice… it’s a different story.

Every time model started generating a longer response,
• CPU jumped from 𝟭𝟬 % to 𝟲𝟬 %
• GPU (NVIDIA MX130) hit 𝟵𝟱% 𝘂𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻.
• Memory climbed to 𝟴.𝟱 𝗚𝗕 / 𝟭𝟲 𝗚𝗕
• Fans roared like a jet engine. 🌀
…and then the model simply stopped generating mid-response. No error. No crash message.
A few seconds later, the fan also slowed down again.

👇 Here’s the before-and-during-generating-longer-response comparison of hardware utilization.

At first, I thought something broke.

But the real reason was far more interesting.

🧠 𝗪𝗵𝗮𝘁 𝗪𝗮𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗛𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴

Even at 1B parameters, the model performs 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗽𝗲𝗿 𝘁𝗼𝗸𝗲𝗻.

Every generated word triggers 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗺𝗮𝘁𝗿𝗶𝘅 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. The GPU handles the heavy computation, while the CPU manages data flow, scheduling, and memory movement. Meanwhile, RAM and VRAM temporarily hold weights, activations, and intermediate states.

In simple terms:
• The 𝗚𝗣𝗨 does intense math.
• The 𝗖𝗣𝗨 coordinates everything.
• 𝗥𝗔𝗠/𝗩𝗥𝗔𝗠 hold model weights and activations.
• The 𝗳𝗮𝗻 is the system’s way of saying, “I’m working really hard.”

When GPU utilization peaks (95%), it produces heat rapidly.
Once temperature crosses a safe limit, 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝘁𝗵𝗿𝗼𝘁𝘁𝗹𝗶𝗻𝗴 activates, a safety mechanism that slows performance to protect the hardware.

This throttling explains why the AI response halted and fan speed decreased shortly after.

📽️ Watch the video below that explains this situation.
Hardware strain during model inference

💡 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗜𝘀 𝗡𝗼𝗿𝗺𝗮𝗹

“Small” in AI doesn’t mean “light” for consumer hardware.
A quick comparison:
• My GPU (MX130) → ~0.4 TFLOPS
• Modern AI GPU (RTX 4090) → ~82 TFLOPS
A gap of nearly 𝟮𝟬𝟬×.

So, even though my laptop is capable of running local inference, it has trouble maintaining it, particularly when the model needs to continue executing billions of operations per second for longer outputs.

🔚 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁

Running AI locally isn’t just about generating text. It teaches you how your CPU, GPU, RAM, and thermal system manage the workload of modern AI models in real time.

Now, every time my laptop fan spins up, I know it’s just trying to think really hard. 😅