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Microsoft recommends more than 120GB of RAM for best performance when running MAI-Code-1.1-Flash locally. That is a recommendation, not a stated minimum: Microsoft also reports a 75.5GB peak-memory measurement for one specific implementation at a 256K context window. The figures describe different things, and neither means every coding task requires a 120GB computer.
What Microsoft means by “more than 120GB”
Microsoft AI’s October 7, 2026 announcement says, “For best performance, we recommend devices with more than 120GB of RAM.” The recommendation is specifically about running MAI-Code-1.1-Flash on a device. It is not presented as a minimum system requirement or a guarantee that any computer above that capacity will perform well.
MAI-Code-1.1-Flash is a coding-focused mixture-of-experts model. Microsoft describes it as having 137 billion total parameters and 6.8 billion active parameters. For local use, the implementation uses quantization and speculative decoding to reduce its memory footprint and improve responsiveness. Microsoft says the on-device model uses 3-bit quantization while retaining a 256K context window. These design choices help make local execution possible, but they do not eliminate the memory required by the rest of the system.
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In a separate October 7 engineering article, Microsoft reports 75.5GB of peak memory usage at a 256K context window for the first shipping version running on Surface Laptop Ultra. The same article describes the quantized on-device model itself as 53GB, an 80% reduction from its bfloat16 cloud variant.
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Those numbers refer to different measures. The 53GB figure is model size, while 75.5GB is a reported peak for that particular implementation and context length. Microsoft says memory is also needed by the operating system, applications, inference runtime, and key-value cache. Context length and workload pressure affect how much memory is used. That helps explain why a device with more capacity than the reported peak may be recommended for best performance: capacity must cover the complete workload, not just the model files.
The 75.5GB result is not a universal memory requirement or a promise that the model will peak at the same amount on another device, runtime, or task. Microsoft’s article also reports prompt-processing throughput of 923.5 tokens per second at 64K context and 769.8 tokens per second at 128K context for its described implementation; these are configuration-specific figures, not expected speeds for every PC.
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What counts as memory on an RTX Spark system
Microsoft describes Surface Laptop Ultra as using NVIDIA RTX Spark with up to 128GB of unified memory. Unified memory is shared across the CPU and GPU, rather than reserved entirely for model weights. Microsoft cautions that not all of it is available to the model. A machine’s advertised total therefore should not be read as the amount the model can use exclusively.
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When comparing a system for local inference, check the details that affect usable capacity and actual workload behavior:
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- Memory architecture: Find out whether the stated capacity is unified/shared memory or a combination of system RAM and dedicated GPU memory.
- Memory available to inference: Account for the operating system, open applications, runtime, and context cache rather than treating the full advertised total as free.
- Context and task size: Longer context and heavier coding tasks can increase memory pressure.
- Device-specific results: Look for measurements on the same hardware and software configuration, including both memory use and speed.
- How Copilot routes work: Local inference can be combined with cloud inference, so decide whether you need to control where a task runs and understand any applicable usage costs.
Do not assume that adding ordinary RAM will satisfy the recommendation. Confirm that the device can be upgraded and that its hardware and inference runtime support the intended model configuration. The cited Microsoft examples focus on unified-memory RTX Spark systems; they do not establish that a generic RAM upgrade will deliver best performance.
How well does the local model perform?
Microsoft reports benchmark results for the full model and its quantized on-device version. In the company’s October 7 engineering article, the SWE-Bench Verified dataset contained 500 tasks; Terminal-Bench 2.1 contained 89. Microsoft reports the following scores:
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| Benchmark | Full MAI-Code-1.1-Flash | GPT-OSS-120B comparator | Quantized on-device model |
| SWE-Bench Verified (500 tasks) | 72.6% | 32.0% | 70.80% |
| Terminal-Bench 2.1 (89 tasks) | 62.9% | 23.6% | 66.29% |
Microsoft says the benchmark tests were run on October 5, 2026, using mixed-precision quantization at approximately 3.3 bits per weight, DFlash2 sliding-window speculative decoding, and a Windows ARM64 llama.cpp CUDA runtime. The company says the results reflect decode throughput for a synthetic code-generation workload and may vary with device, configuration, and other factors. These are Microsoft-published results; the cited material does not establish independent replication.
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Microsoft AI also reports that version 1.1 has 25% greater token efficiency and one quarter the cost compared with the model launched at Microsoft Build in June. It says the model improved Terminal-Bench 2.1 results by 22% in GitHub Copilot CLI and improved .NET tasks by 15%; it also reports a 4% rise in code survival and a 9% increase in return visits. These are company-reported claims, not independent measurements.
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- Before purchasing memory products, please refer to the QVL(Qualified Vendor List) compatibility list first of the motherboard.
- The quality of the CPU memory controller (IMC) and the version from the BIOS of motherboard may both potentially affect the operating frequency of the memory.
Do you need to run it locally?
No. Microsoft says Copilot’s router can choose on-device inference for eligible coding work while using cloud models for tasks that need more capability. The engineering article describes two ways to use it: let Copilot orchestration choose local or cloud inference, or explicitly select a local model for direct control. Local inference does not make the entire Copilot session offline.
Microsoft’s October 7 announcement described experimental access and rollout timing for later in October 2026. Access and availability can change, so check Microsoft’s current announcement and product information rather than assuming the rollout is complete or available to every user.
What hardware did Microsoft announce?
Microsoft’s October 7, 2026 Windows Experience Blog said Surface Laptop Ultra and Surface RTX Spark Dev Box were available for preorder. Its announcement planned Surface Laptop Ultra availability from October 16 and U.S. shipping for the dev box in November. It also named ASUS, Dell, HP, Lenovo, MSI, and Microsoft Surface in its RTX Spark PC ecosystem, with those PCs planned to begin shipping October 16. These were announced plans, not confirmation of current inventory, regional availability, or identical configurations across manufacturers. Check the manufacturer’s current specifications before choosing a system.
Quick Recap
Sources
- Microsoft AI: MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost (October 7, 2026)
- Microsoft Command Line: Bringing local models and sandboxed tools to Windows and GitHub Copilot (October 7, 2026)
- Windows Experience Blog: Building Windows for hybrid intelligence (October 7, 2026)
- Windows Experience Blog: Introducing a powerful new chapter for Windows PCs, accelerated by NVIDIA RTX Spark (May 31, 2026)
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