The Truth About Running AI On Your Own Hardware
Downloading a local model is easy. Running one that is actually useful is a memory
problem, and almost nobody tells you which number decides it.
What running AI locally actually asks of a machine, and where the free part stops. Covers
why memory is the first hard limit rather than processor speed, what quantization buys,
Ollama's own sizing guidance, how VRAM and system RAM stay separate on a PC and why
llama.cpp splitting a model across both turns a hard limit into a slowdown, why an RTX
5090's 32 GB still is not always enough, running with no GPU at all, Apple's unified
memory and the 192 GB Mac Studio, what MLX was doing at WWDC 2026, LM Studio's actual
recommended requirements, the KV cache and why a model that fits at a small context stops
fitting at a large one, Ollama's own 23 GB figure for a local coding model at a 64k
context, where local models still trail frontier ones on long agent runs, what the
hardware really costs once electricity, storage and your own hours are counted, and why
privacy and control are the reasons that actually hold up.
0:00 What local AI actually promises
0:55 What running a model locally means
1:42 The number that matters most
2:41 VRAM against system RAM
3:52 Running without a GPU
4:50 Why Macs are weirdly good at this
5:57 PC against Mac
7:14 How much hardware you actually need
8:12 Context windows are expensive
9:07 Local models against frontier models
10:19 The real cost
11:16 Privacy is the real superpower
12:03 The competition is getting very good
12:47 So what is the truth
Sources and workings: https://codinghorizon.dev/the-truth-a...
@CodingHorizon on X: https://x.com/CodingHorizon
Narration is synthesised.
#localai #ollama #devtools