I’ve been using cloud AI tools for everyday work, but the monthly cost, privacy concerns, and occasional slowdowns are making me look at running a local LLM instead. I’m not sure if the setup, hardware cost, and maintenance are actually worth it compared to cloud AI. If you’ve tried local LLM vs cloud AI, I’d really like help հասկանալ which setup gives the best value, performance, and reliability for real-world use.
I run both. For me, local was worth it for private docs and repeat tasks. Cloud still wins for top quality reasoning and zero maintenence.
My setup:
RTX 4090, 24GB VRAM
64GB RAM
Ollama + Open WebUI
Models: Qwen 2.5 14B, Llama 3.1 8B, Mixtral 8x7B in lower quant
What changed:
Cost. After the GPU buy, monthly cost dropped a lot.
Speed. 8B to 14B models feel fast, often 25 to 70 tok/s on GPU.
Privacy. Local means your files stay on your box.
Reliability. No rate limits, no outage drama.
What did not change:
Best cloud models still write better and reason better.
Local models need tuning, prompt cleanup, model swapping.
You spend time on drivers, VRAM limits, quant choices, context size. It gets annoyng.
My rule:
Use local for summarizing, coding help, RAG over personal files, draft rewrites.
Use cloud for hard analysis, client-facing writing, and anything high stakes.
If you do this cheap, start with what you own. A 3060 12GB is a solid entry point. If you have no GPU, local gets rough fast. CPU-only works, but it feels slow enough to make you quit.
If privacy and fixed cost matter, local is worth it. If you want best output with no tinkering, stay cloud. Hybrid is the sweet spot for most poeple.
I went the other direction from @suenodelbosque for a while and honestly? I sold the local-first dream to myself a bit too hard.
Yes, local is awesome for privacy, offline use, and predictable cost after the hardware hit. But I think people understate the friction. Not just drivers and VRAM, but the constant “is this model actually good enough for this task?” loop. That part gets old fast. You start doing weird little rituals like changing quants, trimming prompts, retrying with another model, then realizing you just spent 20 minutes to avoid using a cloud tool that would’ve nailed it in 30 seconds.
My setup now is boring on purpose:
- modest local box for embeddings, search, and doc Q&A
- cloud for actual thinking-heavy work
- local speech-to-text because that’s one area where local feels genuinly worth it
That split has been worth it. Full local was not, at least for me. Hardware depreciation is real, power draw is real, and if you’re not the kind of person who enjoys tinkering, “ownership” starts feeling like unpaid IT work lol.
My take:
- If your biggest issue is privacy, local makes sense
- If your biggest issue is monthly cost, do the math first
- If your biggest issue is quality, cloud still wins pretty easliy
If you already own a decent GPU, test local. If not, I wouldn’t rush to build a rig unless you know exactly why you need it. Hybrid is less exciting, but probly the least annoying setup.