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Choose RAM and storage around the models and context sizes you expect to use—not a single universal minimum. A practical starting point is 16 GB of system RAM, while recognizing that model fit also depends on GPU memory, quantization, context length, and runtime support. For storage, allow room for the model library as well as Windows, apps, updates, and other files: Ollama says its model files can take tens to hundreds of GB.
How much RAM do you need for local AI?
LM Studio recommends at least 16 GB of RAM on Windows. For Apple Silicon Macs, it recommends 16 GB or more, while noting that 8 GB Macs may still run smaller models with modest context sizes. These are runtime recommendations, not guarantees that every model will fit or perform well. LM Studio’s system requirements page does not display a publication date; the guidance was accessed on October 7, 2026. LM Studio’s system requirements
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Start with the model family and size you want to run, its quantization, the context length you need, and whether you plan to keep multiple models loaded. Then leave memory for the operating system and other open applications. There is no source-supported formula that converts those choices into one exact RAM requirement for every runtime.
System RAM and GPU VRAM are different pools
System RAM is general working memory used by the operating system and applications; CPU inference and some mixed CPU/GPU setups may use it for model work. Dedicated VRAM is memory on a discrete graphics card. LM Studio recommends at least 4 GB of dedicated VRAM for Windows, but that does not mean you can simply add 4 GB VRAM to 16 GB RAM and treat the result as one interchangeable pool. The model, runtime, context, and how much work is offloaded to the GPU all affect what fits.
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Check upgradeability before buying RAM
Before ordering a module or kit, check the exact PC or motherboard specifications. Confirm the memory generation, supported module configuration, maximum capacity, available slots, and whether memory is soldered. The general runtime guidance does not identify a compatible kit for a particular computer.
How much storage do local AI models take?
Budget for the model library, not just the AI program. Ollama’s Windows documentation says its installer needs at least 4 GB for the binary, while model files can require tens to hundreds of GB. The installer figure is not the total space needed to store models. Ollama’s Windows documentation
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Windows requirements are a separate baseline: Microsoft lists 64 GB of storage as the Windows 11 minimum and 256 GB SSD/UFS as the Copilot+ PC minimum. Neither number guarantees enough room for a large model collection. Apps, updates, and personal files also use variable storage, and some Windows features may have additional requirements. Microsoft’s Windows 11 specifications
Estimate capacity from your actual model library
- List the models you expect to keep. Check the storage required for the specific model files you intend to download; total the files you plan to retain at once.
- Reserve space beyond the models. Account for Windows, applications, updates, documents, and other data rather than filling the drive to its nominal capacity.
- Check how the PC can expand. Verify the supported drive form factor, connector, capacity, and external-drive compatibility before choosing an SSD. The cited guidance does not establish a required SSD interface, speed, or endurance for ordinary local LLM use, and it does not make NVMe a requirement for inference.
Move Ollama models to another drive
Ollama documents changing its model location with the OLLAMA_MODELS environment variable. This can be useful when the default location under the user’s home directory is on a crowded system drive. Follow the current Windows instructions in Ollama’s documentation and make sure the chosen drive remains available when you use the models.
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- Do not mix memory kits. Memory kits are sold in matched kits that are designed to run together as a set. Mixing memory kits will result in stability issues or system failure.
How to compare PC configurations for local AI
Compare a candidate PC against the workload and the machine’s real expansion limits, rather than comparing only headline RAM or SSD capacity.
| What to compare | What to check |
|---|---|
| Workload fit | Intended model family and size, quantization, context length, and number of models you expect to load at once. |
| Memory pool | System RAM separately from discrete GPU VRAM or shared/unified memory; do not assume these pools are freely interchangeable. |
| Storage and expansion | Current free space, model-library needs, OS/app/update headroom, internal drive slots, supported formats, and external-drive compatibility. |
| Software support | Whether your chosen operating system, runtime, and model can use the PC’s GPU or NPU. |
| Upgradeability | Whether memory is soldered or replaceable, how many slots are available, and the actual supported maximums for memory and storage. |
Does an NPU change the RAM or storage decision?
An NPU does not help a workload unless its software is written to use that accelerator. Microsoft says NPU support depends on software being specifically programmed for it. Foundry Local detects available hardware and selects a supported execution provider, with CPU fallback among the options; Windows ML can enable inference optimized for CPU, GPU, or NPU according to available execution providers. Confirm that the runtime and model you plan to use support the specific accelerator rather than choosing a PC based only on an NPU TOPS rating. Microsoft’s Copilot+ PCs developer guide and Microsoft’s AI in Windows FAQ
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Microsoft’s Copilot+ PC floor is a 40+ TOPS NPU, 16 GB DDR5/LPDDR5, and 256 GB SSD/UFS. Those figures qualify a system for that device category; they are not a universal local-LLM sizing guide or a promise that every third-party model will run well. Microsoft’s Windows page lists 64 GB as the separate Windows 11 storage minimum. Microsoft’s Windows 11 specifications
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Can local AI run without an internet connection?
Yes, inference can be local after setup, but obtaining models and some updates may require connectivity. Microsoft says Foundry Local inference inputs and outputs stay on the device; initial model downloads and optional catalog refreshes can use the network. LM Studio says offline use is possible once model files have been obtained. Microsoft’s AI in Windows FAQ and LM Studio’s system requirements
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Microsoft’s “Ready-to-use local LLMs on Windows” page, updated January 24, 2026, identifies Phi Silica for Copilot+ PCs and more than 20 open-source LLMs for Windows 10 and later. It also notes that performance varies and not all models are available on all devices. Microsoft’s list of ready-to-use local LLMs
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