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What do you want local AI to do?
Decide on the main workload before comparing tools. A desktop chat interface prioritizes convenient model discovery and conversation; coding or document workflows may depend on integrations; an application that calls a local model needs an API; and a command-line workflow benefits from direct runtime controls.
- Interactive chat: Look for a desktop interface, straightforward model downloads, and controls for context and model selection.
- Coding or document workflows: Check whether the tool supports the integrations your workflow needs, and whether your chosen model is suitable for that task.
- Local application or API use: Confirm that the runtime exposes an API and that its network binding and access controls suit your setup.
- Hands-on experimentation: Consider a runtime that offers control over model files, quantization, backends, and inference options.
Features and integrations vary by tool, so verify that the particular workflow you need is supported rather than assuming every local runtime works the same way.
How much RAM or VRAM do you need to run AI locally?
There is no single RAM or VRAM threshold that applies to all local AI. Memory use depends on the model’s size and format, the context it must handle, the runtime, and whether other applications are using memory. Leave capacity for your operating system and other programs; a model that loads successfully may still run too slowly for your needs.
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Check the chosen runtime’s requirements and the model’s requirements together. On systems with a GPU, distinguish dedicated VRAM from system RAM or unified memory, and verify that the runtime supports the GPU and its software backend. A hybrid CPU/GPU setup can make some larger models usable when they do not fit entirely in GPU memory, but it does not guarantee interactive speed or a particular output quality.
Quantization reduces the memory needed to represent model weights, with trade-offs that depend on the model and format. The llama.cpp project documents quantization from 1.5-bit through 8-bit integer formats and CPU/GPU hybrid inference; these capabilities do not establish a universal model-to-GPU sizing rule. See the llama.cpp README for its formats and backend details.
LM Studio’s published requirements are product-specific
LM Studio’s official requirements are useful as a concrete example, not as minimum requirements for every local AI tool or model. The page recommends 16GB or more RAM on supported platforms and describes exceptions and platform-specific requirements:
- Apple Silicon Mac: M1, M2, M3, or M4 with macOS 14 or newer. LM Studio says 8GB Macs may work with smaller models and modest context sizes; Intel-based Macs are currently unsupported.
- Windows: x64 systems need AVX2; Snapdragon X Elite ARM systems are also supported. LM Studio recommends at least 16GB RAM and at least 4GB dedicated VRAM.
- Linux: x64 and ARM64 are supported. The stated distribution requirement is Ubuntu 20.04 or newer; the page notes that versions newer than 22 are not well tested.
These are the requirements and recommendations published by LM Studio, not a guarantee that a particular model or workload will run well. Check the current LM Studio system requirements before installing.
Which kind of local AI tool fits your workflow?
Compare the user experience you want with the control and integration you need. The three options below differ in emphasis; none is a universal best choice.
| Tool | Best fit | What it offers | What to verify |
|---|---|---|---|
| LM Studio | A beginner-friendly desktop workflow | Chat, model search and downloads through Hugging Face, local 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. | Its operating-system and hardware requirements, model format, and whether the integrations and endpoint behavior you need are supported. Details are in the LM Studio documentation. |
| Ollama | Local model management, command-line use, or application integration through an API | A local HTTP server and API, with settings for context, model retention, concurrency, storage locations, and network binding. | Installation and acceleration support for your operating system, and the server’s binding and access configuration. Consult the Ollama FAQ. |
| llama.cpp | Users who want more direct runtime control | GGUF models, quantization, multiple device backends, command-line and server tools, and hybrid CPU/GPU inference. | Model-file management, runtime options, and the build and device support for your platform. Its README lists backends including Metal, CUDA, HIP, Vulkan, and SYCL. |
The documentation describes features, not an independently benchmarked comparison of speed or output quality. Choose based on the workflow and controls you need rather than assuming one runtime is faster or produces better answers.
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Check your operating system, model format, and integrations
Before downloading a model, verify that the runtime supports your operating system and accelerator, and that it can load the model’s format. A model available for one format or backend may not be usable in another tool or on another device. Also consider whether you are comfortable with a graphical interface or prefer a command line, and whether you need a local API, MCP connections, or other integrations.
For llama.cpp, the listed backends include Metal for Apple Silicon, CUDA for NVIDIA GPUs, HIP for AMD GPUs, Vulkan, and SYCL, among others. That list does not establish that every device or build will work on your machine; check the project’s current documentation for the platform and configuration you plan to use.
The Tool Desk
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Local inference means the model can process prompts on your computer, but it does not by itself prove that the computer is isolated from the network. Downloads require a connection, and network exposure or optional integrations can change where data goes.
LM Studio says it can operate entirely offline once model files are available; its system requirements page links to its offline-operation guidance. Ollama’s FAQ says prompts and answers are not sent back to ollama.com because Ollama runs locally. It also says Ollama binds to 127.0.0.1 by default and documents changing that address. Exposing a local service through a proxy or tunnel changes who may be able to reach it, so review network settings and integrations before using sensitive information.
Should you upgrade your computer or buy a graphics card?
Check the model and workload you actually intend to run against your current computer before making a hardware purchase. If the runtime and model fit, an upgrade may be unnecessary. If they do not, compare a prospective computer or graphics card against the chosen tool’s requirements and the workload’s memory needs—not a generic local-AI minimum.
For a graphics card, check dedicated VRAM, compatibility with the runtime’s supported backend, and practical constraints such as power and physical space. LM Studio recommends dedicated VRAM for Windows, and llama.cpp documents GPU backends and hybrid CPU/GPU inference, but neither establishes a particular card as the right choice or guarantees a performance result. Treat a purchase as conditional on the model, format, and workload you plan to use.
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