I’ve been digging into the local AI hardware question pretty deeply lately, and it’s one of those topics where the answer is frustratingly — but honestly — “it depends.”
The hype cycle around running your own models has hit that inflection point where everyone’s either telling you it’s amazing or telling you it’s a waste of money. I don’t know many people with a couple of weeks under their belt actually running serious models on home hardware, which means most of the takes out there are theoretical. So here’s my attempt to cut through it.
The Hardware Reality, by Model Size
Let’s start with what “running the full model” actually means, because GGUF quantized versions and full-weight models are very different conversations.
7B parameter models — these are your entry point. A $1,500 MacBook Air with 32GB of unified memory will handle these. You’ll get reasonable inference speeds, and for a lot of use cases — code completion, summarization, quick Q&A — a well-tuned 7B model is genuinely useful. This is the sweet spot for people who want to experiment without a significant hardware investment.
70B parameter models — now you’re in different territory. You probably need 128GB of RAM to run these at full precision, and that means you’re looking at a maxed-out Mac Studio or a serious workstation build. We’re talking $4,000-$7,000 depending on your config. The performance is noticeably better than 7B for complex reasoning tasks, but the cost-per-capability jump is steep.
1T+ parameter models — this is where it gets genuinely interesting and genuinely expensive. People ARE doing it with around $40K worth of Mac Studios linked together. But here’s the thing — they’re getting roughly a third of the speed of a cloud service. So you’re spending the money, you’re dealing with the complexity of networking multiple machines together, and your reward is.. slower inference than just hitting an API.
That math doesn’t work for most people.
The Question Nobody’s Asking Loud Enough
Here’s what I keep coming back to: do you REALLY need to run models of that size locally?
I think for most use cases, the honest answer is no. The 7B and 13B class of models have gotten remarkably capable. If you’re running local models for privacy, for offline access, for the ability to fine-tune on your own data — those are all legitimate reasons. But you don’t need a trillion-parameter model for any of them.
The people pushing toward massive local deployments are often chasing benchmark numbers rather than solving actual problems. A well-prompted, well-fine-tuned smaller model running fast on modest hardware will beat a massive model running at a third of cloud speed for most practical workflows.
The Middle Path Worth Watching
This is where things get interesting from a cost perspective. I’ve been looking at Moonshot’s V2.5 model, and the economics are pretty compelling. It’s roughly a fifth of the cost of Claude on a per-token basis, which puts it in a price range that’s comparable to running your own hardware — but without the hardware.
Think about that for a second. If your motivation for running locally is cost savings on API calls, you might be able to get similar economics from a cheaper cloud API without buying any hardware at all. I need to dig deeper into the actual capability comparison, but on paper, it’s worth serious consideration as a middle ground.
The Tooling Layer Matters More Than the Model
One thing I’ve been increasingly convinced of is that the tooling and orchestration layer on top of models matters at least as much as the raw model capability. Projects like superpowers are pushing this idea pretty hard — that the real unlock isn’t a bigger model, it’s a smarter system around the model you’ve already got.
This tracks with what I’ve been seeing in my own work. I’ve written before about setting up Claude for coding workflows, and the orchestration — the context management, the tool integration, the pipeline design — that’s where the leverage actually lives. A 7B model with great tooling will outperform a 70B model you’re just chatting with in a terminal window.
My Take
If you’re thinking about getting into local model hosting, here’s how I’d approach it in early 2025:
Start small. Get a 32GB MacBook or equivalent and run some 7B models. See if local inference actually fits your workflow, or if you’re just excited about the idea of it. Plenty of people set up elaborate local AI rigs and then go right back to Claude or GPT because the experience is better.
Don’t chase size. The jump from 7B to 70B is expensive and the jump from 70B to 1T is absurd for personal use. Unless you have a very specific use case that demands it, the cost-performance ratio falls off a cliff.
Watch the API market. Moonshot and similar providers are driving prices down fast. The economic case for local hosting gets weaker every quarter as cloud inference gets cheaper. What makes sense today might not make sense in six months.
Invest in tooling, not hardware. Whatever model you’re running — local or cloud — the orchestration layer is where you’ll see the biggest returns. That’s where I’d put my time and energy.
The local AI movement is real and it’s important — there are genuine reasons to want models running on your own hardware. But the current discourse has too much “bigger is better” energy and not enough honest assessment of what most people actually need. For the vast majority of use cases, a modest local setup plus smart tooling plus a cheap API fallback is going to outperform a $40K home cluster running at a third of cloud speed.
Be practical. Start small. Iterate from there.
