Deploying LLMs and AI Agents Locally — Is It Actually Worth It?
Running AI models locally sounds appealing in theory — no API costs, no data leaving your machine, full control. But is it actually practical for everyday development work? The honest answer is: it depends. Here's how to think about it.
When local deployment makes sense
Privacy is non-negotiable. If you're working with sensitive data — client information, proprietary code, internal documents — sending that to a third-party API isn't always acceptable. Running a model locally means your data stays on your hardware, full stop.
You need it to work offline. Building something that needs to function without an internet connection? A locally running model is the only option. This matters more than people expect: flights, travel, unreliable connections, client sites with locked-down networks.
You want to experiment freely. With a local setup, you can run hundreds of requests, test prompts aggressively, swap between models, and iterate without watching a usage meter. The cost is paid upfront in hardware, not per token.
Latency matters at the application level. For some use cases — especially real-time coding assistance or agent loops that make many sequential calls — local inference can be faster than a round trip to a remote API, even if the raw generation speed is slower.
When it doesn't make sense
You need frontier model capability. GPT-4o, Claude Opus, Gemini Ultra — these are not replicable locally with consumer hardware today. If your task genuinely requires the best available reasoning, a local 70B model will underperform a hosted frontier model.
Your hardware is underpowered. A 7B model running on 8 GB of VRAM with constant layer offloading to RAM is a frustrating experience. Slow, unreliable, and ultimately not useful for real work. If you don't have adequate hardware, the local path will disappoint.
Setup time isn't worth it for your use case. If you just need a capable AI assistant for occasional tasks, paying for a Claude Pro or ChatGPT Plus subscription is far simpler and more capable than spending a weekend configuring a local setup.
What are the alternatives?
If fully local isn't right for you, there's a middle ground worth considering.
Self-hosted on a VPS or cloud GPU. Rent a GPU instance (Lambda Labs, Vast.ai, RunPod) and run your own model on rented hardware. You get privacy from third-party AI providers while offloading the hardware cost. Pay only for what you use.
Open-weight models via API. Services like Together AI, Groq, and Fireworks AI offer hosted inference for open-weight models like Llama 3, Mistral, and Qwen at low cost. You get the privacy benefit of open models without owning the hardware.
Hybrid approaches. Use a local model for tasks that handle sensitive data or need to work offline, and route other requests to a hosted API when you need more capability.
The realistic picture
For most developers, a local setup with a well-chosen 7B–14B model covers a surprisingly large percentage of day-to-day AI tasks: code completion, summarisation, question answering, document processing, and running agent workflows. The gap between open-weight models and frontier models has narrowed significantly.
It's not a replacement for hosted frontier models — it's a complement. The rest of this series is built around making that complement as capable and reliable as possible.