Start with the job you want AI to do, then check which tools and models your computer can actually run. Chat, coding, document work, and serving a local API call for different features; memory needs also change with model size, context length, and workload. There is no single minimum specification for running AI locally.
What do you want local AI to do?
Choose the workflow before choosing the software. A desktop chat interface prioritizes convenient model discovery and conversation; coding or document workflows may depend on integrations; an application that needs a local API requires a runtime that exposes one. A command-line setup can offer more direct control, but asks you to manage more details yourself.
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- Interactive chat: Look for a usable desktop interface, straightforward model downloads, and support for the model formats you plan to use.
- Coding or document workflows: Check whether the tool supports the integrations your applications need, and whether the selected model can handle your expected context.
- Local application integration: Confirm that the runtime provides an API compatible with the application, and understand how its server is bound to the network.
- Custom runtime control: If you are comfortable managing model files and options, a lower-level runtime may better suit your needs.
LM Studio documents chat, model search and downloads through Hugging Face, model management, MCP server connections, and local or network OpenAI-like endpoints. It supports llama.cpp GGUF models on Mac, Windows, and Linux, and MLX models on Apple Silicon; see its application documentation. Ollama documents a local HTTP server and API, along with controls for context, model retention, concurrency, and network binding in its FAQ. For direct runtime control, llama.cpp documents GGUF models, command-line and server tools, quantization, and several accelerator backends.
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Begin with the operating system and the specific tool’s current support—not a generic claim about what “local AI” needs. Product requirements differ, and a supported computer may still be constrained by the model and workload you choose.
LM Studio’s documented platform requirements
LM Studio’s system requirements specify Apple Silicon Macs with M1, M2, M3, or M4 and macOS 14 or newer; Intel-based Macs are currently unsupported. The page recommends 16GB or more of RAM, while noting that 8GB Macs may work with smaller models and modest context sizes.
On Windows, LM Studio supports x64 and Snapdragon X Elite ARM systems. Its x64 support requires AVX2; the page recommends at least 16GB of RAM and at least 4GB of dedicated VRAM. Linux support includes x64 and ARM64, with Ubuntu 20.04 or newer specified; the page notes that newer versions than 22 are not well tested. These are LM Studio-specific requirements and recommendations, not universal thresholds for all local-AI software.
Check the accelerator and runtime
Hardware support also depends on the runtime’s backend and build. The llama.cpp README lists Metal for Apple Silicon, CUDA for NVIDIA GPUs, HIP for AMD GPUs, Vulkan, SYCL, and other backends. Verify support for your particular device and platform rather than assuming that a listed backend works with every installation.
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llama.cpp also documents CPU/GPU hybrid inference, which can offload some work to a GPU when a model exceeds its available VRAM. That flexibility can make running a model possible on constrained hardware, but it does not guarantee interactive speed or any particular output quality.
How much RAM or VRAM do you need?
There is no dependable one-number answer. Memory use depends on the model’s weights and quantization, context length, modality, and how many requests or models you run at once. The operating system and other open applications need memory too, so do not treat a model’s advertised size as the entire computer requirement.
- Choose a model and workload. Decide whether you need chat, coding, document analysis, or another task, and estimate how much context and concurrency you expect.
- Check the model format and memory needs. Confirm that your chosen runtime supports the model and its format; then compare its documented requirements with your available system RAM and GPU memory or unified memory.
- Leave capacity for the rest of the system. A configuration that barely fits a model may leave too little room for the operating system, other applications, or longer contexts.
- Try the current computer before buying hardware. Check the tool’s requirements and test the intended model and workflow where possible. Decide what is inadequate—memory capacity, compatibility, or usable speed—before considering an upgrade.
Quantization reduces memory use by representing model weights in lower-bit formats. The llama.cpp README describes formats from 1.5-bit through 8-bit integer, as well as CPU/GPU hybrid inference. These options expand the configurations a runtime may accommodate; they are not guarantees of speed or output quality for a particular model and machine.
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Which kind of local AI tool fits your workflow?
| Tool style | Useful when | What to verify |
|---|---|---|
| LM Studio desktop app | You want a graphical chat workflow, model discovery and management, or documented app connections. | Its platform requirements, supported model formats, and whether its local or network endpoints suit your setup. Details are in the app documentation and system requirements. |
| Ollama | You need a local HTTP server or API for command-line use or application integration. | The OS-specific installation and acceleration documentation, API fit, model settings, and server binding options described in the FAQ. |
| llama.cpp | You prefer more direct control over model files, runtime options, backends, or CPU/GPU operation. | Build and device support on your platform, supported GGUF model, and the setup and troubleshooting you are prepared to handle. Consult the upstream README. |
No single option is best for every reader. Compare task fit, operating-system and accelerator support, available memory, model formats and availability, download and update workflow, context and concurrency needs, interface versus command line, API and integrations, and tolerance for setup work.
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Does running a model locally keep it private and offline?
Local inference means the model runs on your machine; it does not by itself prove the whole computer is isolated from networks or third-party services. Downloads require network access, and optional integrations or an exposed local server can change where data can go.
LM Studio’s requirements page links to offline-operation guidance and says the app can operate entirely offline once model files are available. Ollama’s FAQ says prompts and answers are not sent back to ollama.com because Ollama runs locally. It also documents that its server binds to 127.0.0.1 by default and that the bind address can be changed; a proxy or tunnel can expose the service. Check your own binding and integrations rather than treating “local” as synonymous with “air-gapped.”
Should you buy a graphics card or a new computer?
Only consider a hardware purchase after selecting the runtime, model, and workload. A graphics card with dedicated VRAM may be relevant if the chosen tool supports it and memory is the limiting factor; LM Studio recommends dedicated VRAM for Windows, while llama.cpp documents GPU backends and hybrid CPU/GPU inference. Neither source establishes a particular card as the right choice or promises a performance result.
Before spending money, compare the prospective hardware’s memory capacity and compatibility with the tool, as well as your computer’s power and physical-space constraints. If the current machine fails the chosen tool’s requirements, a compatible computer may be an option, but no specific model follows from these requirements alone.
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