What Microsoft is planning
Microsoft’s October 7 post describes a local version of MAI Code 1.1 Flash and planned Copilot coordination between local and cloud inference by the end of October. It explains that model weights share a memory budget with applications, the runtime, and the cache used for growing context. The post also warns that local inference does not make a session offline. These are Microsoft’s descriptions of an upcoming experience, not our independent performance measurements.
Source: Microsoft: Bringing local models and sandboxed tools to Windows and GitHub Copilot ↗
Our take: shop for the task, not the biggest number
The interesting question for a student project is how the entire job behaves. Does an assistant remain responsive after it reads several files? Can you keep your editor, browser, and other tools open? Does checking and repairing its work consume the time you thought you saved? A fast-looking clip does not answer those questions.
Our buying advice is to write down your actual workflow before deciding you need new hardware. A weekend website, an enormous codebase, and an offline experiment have different requirements. If you already have a working setup, use it to establish a baseline. Keep the project, prompt, and acceptance checks consistent when comparing another option.
Try a realistic comparison
Choose a small task you can judge, such as adding keyboard navigation to a sample page. Save the starting files and write three behavior checks. Record how long it takes to reach a version that passes those checks, including your review and any fixes. Note whether ordinary apps become sluggish during the run.
For an available local setup, separately check where model inference happens and what the assistant’s tools can access. Do not assume the word local answers both questions. Our suggested comparison is about the finished workflow, not collecting a flattering speed number. If a new setup does not improve that workflow enough to matter, waiting is a perfectly useful decision.
Try this, then make it yours.
Write one repeatable coding task and compare time to a verified result before buying hardware.
Explore the tool ↗Follow the signal.
Our reporting starts here. Practical suggestions are our analysis, and vendor performance statements are claims unless independently verified. We haven’t hands-on tested this release.
