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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFor a local AI PC, size memory and storage around the models and context lengths you plan to use—not one universal “AI-ready” spec. A practical starting point is 16 GB or more of system RAM, then check whether your chosen runtime and workload need dedicated GPU memory; reserve SSD capacity for the model library as well as Windows, apps, updates, and personal files. LM Studio recommends at least 16 GB RAM on Windows and 16 GB+ on Apple Silicon, but those are runtime guidelines, not a promise that every model will fit or run well.
Start with the workload, not a single RAM number
Before buying or upgrading, identify the model family and size, quantization, context length, and whether you expect to keep more than one model loaded. These factors affect memory use, and the runtime’s CPU/GPU offload options matter too. There is no reliable universal formula in the available official guidance that converts a model name into an exact RAM or VRAM requirement.
Then leave headroom for the operating system and the apps you keep open. A machine that can load a model may still feel constrained if the model, context, OS, and other applications compete for memory.
Understand RAM, VRAM, and unified memory
System RAM is the computer’s general working memory. CPU inference uses it, and some mixed CPU/GPU configurations may also use system memory. Dedicated VRAM is a separate pool on a discrete graphics card; do not add RAM and VRAM together as if they were interchangeable.
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LM Studio’s current requirements page recommends at least 16 GB of RAM and at least 4 GB of dedicated VRAM for Windows. For Apple Silicon Macs, it recommends 16 GB or more of RAM, while noting that 8 GB Macs may still work with smaller models and modest context sizes. These are LM Studio recommendations, not guarantees for all models or runtimes. See LM Studio’s system requirements.
Check whether an upgrade is possible
Before ordering memory, check the exact PC or motherboard specifications. Confirm the memory generation, supported module configuration, maximum capacity, available slots, and whether memory is soldered. A recommendation for more RAM does not identify a compatible kit for your particular computer.
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Plan SSD space for the model library
Model files can take far more space than the runtime itself. Ollama’s Windows documentation says its binary installation needs at least 4 GB, while model storage can range from tens to hundreds of GB. The documentation places models and configuration under the user’s home directory by default and explains how to change the model location with OLLAMA_MODELS. See Ollama’s Windows documentation.
Windows 11’s 64 GB storage minimum is an operating-system baseline, not a sufficient-capacity recommendation for a model collection. Microsoft lists 256 GB SSD/UFS as the storage floor for the Copilot+ PC category; that, too, is a device-category requirement rather than a guarantee that a particular library will fit. Windows apps, updates, and other files also consume variable space. See Microsoft’s Windows 11 specifications.
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Estimate the capacity you actually need
- List the models you intend to keep. Use the actual model-file sizes for your chosen downloads rather than assuming all models of a given family occupy the same space.
- Add operating-system, app, update, and personal-file headroom. Avoid treating the drive’s advertised capacity as entirely available for models.
- Check expansion options. Verify the PC’s supported drive form factor, connector, capacity, and external-drive compatibility before choosing an internal or external SSD.
- Relocate model storage if needed. Ollama documents setting
OLLAMA_MODELSto place its model files on another suitable drive.
The cited guidance does not establish a required SSD interface, speed, or endurance for ordinary local LLM inference, so NVMe should not be treated as a universal requirement. Prioritize sufficient compatible capacity and available headroom.
Check accelerator support before paying for an NPU
An NPU’s TOPS rating alone does not tell you whether your chosen local AI software will use it. Microsoft says software must be specifically programmed to use an NPU. Its Foundry Local runtime selects a supported execution provider based on available hardware and can fall back to CPU; Windows ML similarly supports inference paths optimized for CPU, GPU, or NPU according to available execution providers. Confirm that your intended runtime and model support the accelerator on your OS and device.
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Microsoft’s Copilot+ PC baseline is a 40+ TOPS NPU, 16 GB DDR5/LPDDR5, and 256 GB SSD/UFS. Those specifications qualify a system for that device category; they do not ensure that every third-party local model will run well. Microsoft’s Copilot+ PCs developer guide explains the NPU software requirement, and its Ready-to-use local LLMs on Windows page, last updated January 24, 2026, describes Phi Silica for Copilot+ PCs and more than 20 open-source models for Windows 10 and later. Availability varies by device, and performance varies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Know what “local” means for connectivity
Local inference can work without a network connection after setup, but obtaining model files and some software or catalog updates may require connectivity. LM Studio says offline use is possible once model files have been obtained. Microsoft says Foundry Local inference inputs and outputs stay on-device, while initial model downloads and optional catalog refreshes can involve network traffic. Details are in Microsoft’s FAQ about AI in Windows apps and LM Studio’s system requirements.
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Choose a configuration by balancing the constraints
| What to assess | What to check |
|---|---|
| Workload fit | Model family and size, quantization, context length, and number of simultaneously loaded models. |
| Memory pool | System RAM versus dedicated GPU VRAM or Apple unified memory; capacity in one pool does not automatically replace capacity in another. |
| Storage capacity | Current free space, intended model library, and room for the OS, apps, updates, and other data. |
| Expandability | Supported drive form factor, connector, capacity, and external-drive use; for RAM, slots, soldering, module configuration, and supported maximum. |
| Software support | Whether the selected runtime and model can use the GPU or NPU on the chosen OS and device. |
Use the model and context you actually expect to run to set the target, then check the runtime’s guidance and your PC’s upgrade limits. If your plans are uncertain, prioritize a machine with enough memory and storage for your likely near-term use and a verified upgrade path rather than relying on a certification label or a single headline number.
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