Learn How AI Hardware Works
Think of a computer running an AI model like a busy kitchen. These analogies are simplified teaching tools, not literal descriptions — but they capture why each piece of hardware matters.
CPU
Actual function: Runs the operating system and coordinates everything else in the computer.
Why it matters for AI: The CPU hands work off to the GPU and manages data flowing in and out, but it isn't what does the heavy AI math.
Measurement that matters: Clock speed and core count.
Example: The CPU reads your question, prepares it, and passes it to the GPU team to actually answer.
CPU Cores
Actual function: Each core can run a separate stream of instructions at the same time.
Why it matters for AI: More cores help handle multiple requests and background tasks, but again, not the core AI math itself.
Measurement that matters: Core count.
Example: One cook preps vegetables while another watches the oven — different jobs, same kitchen.
System RAM
Actual function: Fast temporary storage the CPU uses while working.
Why it matters for AI: Useful for staging data, but too slow to hold the model itself during heavy use — that's the GPU's job.
Measurement that matters: Gigabytes (GB) of RAM.
Example: Counter space near the stove where a cook sets out ingredients before cooking.
GPU
Actual function: Thousands of small processing units doing the same kind of math simultaneously.
Why it matters for AI: This is exactly the kind of math models need — the GPU is what actually "runs" the model.
Measurement that matters: GPU memory (GB) and raw compute performance.
Example: A whole line cook station working through orders in parallel instead of one cook doing everything alone.
GPU Memory
Actual function: Holds the model's parameters and working data while it's running.
Why it matters for AI: If the model doesn't fit in GPU memory, it simply cannot run — there's no substitute counter space.
Measurement that matters: Gigabytes (GB) of GPU memory.
Example: A 397 GB model needs about 476 GB of counter space once you add 20% working room.
Storage
Actual function: Long-term storage (SSD/HDD) for model files, logs, and data, even when the power is off.
Why it matters for AI: The model has to be loaded from storage into GPU memory before it can run.
Measurement that matters: Storage capacity (TB) and read speed.
Example: The pantry where ingredients wait until they're pulled out and prepped.
Networking
Actual function: Moves data between machines quickly, especially when several systems share one job.
Why it matters for AI: Splitting a model across machines only works if the connection between them is fast enough.
Measurement that matters: Bandwidth (Gbps).
Example: A fast hallway lets one kitchen hand off a half-finished dish to another kitchen without slowing service.
Cluster
Actual function: Multiple machines networked together to act as one larger system.
Why it matters for AI: Needed when one machine's GPU memory or capacity isn't enough for the model or the traffic.
Measurement that matters: Combined GPU memory and combined power across every machine.
Example: Four connected kitchens, each with their own team, serving one very large banquet together.
Cooling
Actual function: Fans, heatsinks, or liquid cooling that remove the heat generated by hard-working chips.
Why it matters for AI: GPUs draw a lot of power and generate a lot of heat — without cooling, they'd throttle or fail.
Measurement that matters: Thermal design power (TDP, in watts) and cooling capacity.
Example: A kitchen's exhaust hood keeping the room usable while every burner runs at once.
Power
Actual function: Delivers the electricity every component needs, continuously, without interruption.
Why it matters for AI: Datacenter-grade AI hardware can draw as much power as several homes — power delivery has to be planned for.
Measurement that matters: Watts (instantaneous) and kilowatt-hours (kWh, over time).
Example: A 10,200-watt system draws about as much power as 8.5 average homes, continuously.