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Microsoft's official October 7 announcements confirm local MAI-Code-1.1-Flash for Windows PCs and GitHub Copilot on high-end hardware.

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Home/Tech/Microsoft preparing local MAI-Code-1.1-Flash for high-end PCs
VERIFIEDBy Xavier Rivera· ·1.5 min read

Microsoft preparing local MAI-Code-1.1-Flash for high-end PCs

Microsoft has introduced a local edition of MAI-Code-1.1-Flash that runs on Windows PCs without server access. The model requires substantial memory and is confirmed in development for GitHub Copilot by the end of October on Nvidia RTX Spark hardware.

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Microsoft preparing local MAI-Code-1.1-Flash for high-end PCs
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TL;DRAI · 60 sec read

Microsoft introduces a local MAI-Code-1.1-Flash coding model that runs on Windows PCs without servers. It targets GitHub Copilot on Nvidia RTX Spark hardware and incurs no token costs. High hardware demands limit use to high-end machines needing over 120 GB video memory. The model trails leading cloud versions on benchmarks.

Microsoft has introduced a local version of its MAI-Code-1.1-Flash AI coding model that runs directly on Windows PCs without using company servers.

Model confirmed in development for GitHub Copilot by end of October. The local edition is planned for GitHub Copilot on PCs equipped with Nvidia RTX Spark chips. Computations occur on the device itself, so Microsoft states users will not be billed for tokens.
The model requires substantial memory to load, and Microsoft recommends more than 120 GB of video memory for acceptable performance.

Hardware demands restrict availability to high-end machines. The model requires substantial memory to load, and Microsoft recommends more than 120 GB of video memory for acceptable performance. This currently limits the experience to configurations such as the Surface Laptop Ultra with 128 GB unified memory.

Performance trails leading cloud models on key benchmarks. Microsoft reports a 62.9 percent success rate on Terminal-Bench 2.1. On the same test, Claude Opus 5.5 scores 87.6 percent and GPT-6 Astra scores 87.3 percent. On SWE-Bench Verified the local version reaches 70.8 percent while the cloud version reaches 72.6 percent.
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Model uses mixture-of-experts architecture. MAI-Code-1.1-Flash contains 137 billion total parameters yet activates only 5 billion at each step. The model first appeared on August 11 and entered GitHub Copilot this summer at $0.20 per million input tokens and $1.20 per million output tokens.
Computations occur on the device itself, so Microsoft states users will not be billed for tokens.
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Local execution eliminates token costs. Microsoft positions the offline capability as an alternative to cloud services such as Claude Haiku 5.5 and GPT-6 Luna. The company continues broader work to enable large models to run locally on Windows.
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