Okay so — you opened the model picker in PyCharm or IntelliJ, the little dropdown next to the Copilot chat box, and there's a new name in the list you didn't put there. Ollama, sitting right next to Claude and GPT like it's always been there. Nobody popped up a tutorial. It's just an option now, and you're standing at a fork nobody warned you about: download a five-to-eight-gigabyte file to your own laptop right now, or leave it alone and keep doing what you were doing.
I'm not going to hype you into downloading anything, and I'm not going to talk you out of it either. What I'm going to give you is the actual tradeoff, in plain terms, and a rule you can apply in the next thirty seconds without reading a benchmark table nobody can verify.
What GitHub actually shipped
On August 11, 2026, GitHub added Ollama as a bring-your-own-key provider inside Copilot for JetBrains IDEs — IntelliJ, PyCharm, WebStorm, the rest of the family. Bring-your-own-key, BYOK for short, just means a model you connect yourself instead of one GitHub hosts and bills you for. Copilot already had cloud models sitting behind your subscription; this didn't replace any of them. It just added a slot in the same list for a model running on your own machine, alongside Claude and GPT rather than instead of them.
That's the whole news event. No benchmark, no claim that local beats cloud, just a new door in a picker you already had open. What you do with the door is the actual question, and that's where the hype usually takes over from the reasoning.
What "local" actually buys you
Here's the mechanism, because the tradeoff only makes sense once you see it. Ollama is a small program that runs on your computer and starts a server, by default at localhost:11434 — a web address that never leaves your machine, the same way a page you're editing in a local dev server never leaves it. You tell it to "pull" a model, which downloads that model's weights — the actual numbers that make it work — to your disk. Then when Copilot needs an answer, instead of sending your code to Anthropic's or OpenAI's servers, it sends it to that address on your own laptop, and your own laptop's processor and memory do the thinking.
Nothing about your code crosses your network connection in that setup. That's the entire pitch for local, in one sentence, and it's a real one — not marketing.
The honest tradeoff, no vibes
Local, plainly:
- Free per request. You already own the electricity; there's no metered API call behind it.
- Your code never leaves your machine. If you're working with anything an employer, a client, or you personally don't want touching someone else's servers, this is the actual answer, not a preference.
- You pay for that in three ways instead: the setup itself, the RAM it eats while running, and a model that is, plainly, not as capable as what you're used to. A model small enough to fit on a laptop is a smaller, less capable version of the giant ones running in a data center — that gap is real and it doesn't close because you wanted it to.
Cloud, plainly:
- It works the moment you click it. No download, no local server, no "did it actually load."
- It's smarter — bigger model, more consistent answers, better at the exact kind of "explain why this broke" question you'll be asking constantly while you're learning.
- You pay per request, metered against a subscription or a usage window, and your code does leave your machine — it's on someone else's server while it's being read.
Neither side is the trick answer. They're solving different problems, and most of the SEO listicles you'll find get so busy proving one side "wins" that they never say that plainly.
Check your RAM before you download anything
Before you pull anything, know what you're actually working with. On Linux or WSL, this tells you:
free -h
On my machine right now that prints:
total used free shared buff/cache available Mem: 15Gi 1.1Gi 12Gi 4.9Mi 2.7Gi 14Gi
Yours will show different numbers — what matters is the "total" column. On a Mac, Activity Monitor's Memory tab shows the same thing; on Windows, Task Manager's Performance tab does.
The rule of thumb that gets repeated across Ollama guides, for what it's worth as a rough floor rather than an exact spec: a smaller, 7-to-8-billion-parameter model wants somewhere around 8GB of RAM to run without choking your whole machine, and a mid-sized 13-billion-parameter model wants closer to 16GB. If your total is sitting at 8GB and Chrome is already open, you're not running a local model comfortably today — that's not a judgment, it's arithmetic. Coding-focused model families like Qwen2.5-Coder or Code Llama exist specifically because a smaller model tuned on code punches above its size on programming tasks, which is worth knowing before you assume "small" means "useless."
The actual decision rule
Pick local when:
- You've hit a usage cap mid-lesson more than once and it's happening on the free or cheap tier, not because you're doing something unusually heavy.
- The code you're working on is genuinely sensitive — an employer's repo, something under an NDA — and "it stays on my machine" isn't a nice-to-have, it's the requirement.
- You have 16GB of RAM or more to spare, and you're curious enough to eat an evening on setup as its own small project, not because you need the answer tonight.
- You're doing boilerplate-shaped work — scaffolding, syntax you already mostly know, quick sanity checks — where a smaller model's ceiling doesn't matter because you're not asking it to reason hard.
Pick cloud when:
- You're still learning the fundamentals and you need the model to actually be right, not just fast and free — a wrong explanation costs you more time than the metered request ever would.
- Setup itself is the barrier between you and finishing tonight's lesson. A cloud model answering in your existing Copilot tab beats a perfect local one you never got working.
- Your machine has 8GB of RAM and a browser tab habit. That's not a local-model machine yet, no matter what the listicles imply.
- You're asking "why did this break" — the exact question where a smaller model's gaps show up hardest, right when you can least afford a shaky answer.
If you're the person who hit a Claude or Copilot usage cap mid-lesson and is wondering whether local fixes that for good: it can, for the boilerplate half of your work, on a machine with the RAM to spare. It won't replace the cloud model for the half of your learning that's actually "explain why," which is most of it right now.
Why this matters
The question "local or cloud" gets asked like it's a religious commitment — pick a side, defend it forever. It isn't one. It's a tool choice, and most people who've actually replaced part of their daily coding with a local model didn't do it all at once — they moved the boring, mechanical half over and kept the cloud model for the half that needed to actually think. That's not a compromise, it's just using the right tool for the size of the question you're asking.
Final thought
Run 'free -h' — or open Activity Monitor or Task Manager — before you touch that new Ollama entry in the picker. If you're under 16GB, close the tab and keep going the way you were; nothing about this week's update changes what you should be doing. If you've got room to spare, pull one small coding-focused model, point one boilerplate task at it, and leave the "why did this break" questions going to the cloud model like you already were.