You can set up a local AI assistant by installing a model runner, downloading model weights, loading a model, and chatting with it on your computer. For a beginner-friendly desktop workflow, use LM Studio; if you prefer commands, Ollama offers a command-line setup. Your hardware determines which models are practical and how quickly they respond. Local inference can keep prompts on your device, but downloads need internet, and cloud services you select separately may still receive data.
What “local AI assistant” means
A model runner loads a model’s weights and uses your computer’s memory and processing hardware to generate responses. You can chat in the runner itself; an additional interface is optional. “Local” describes where a particular model runs, not necessarily every feature or service connected to your chat.
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Model weights are commonly distributed in formats such as .gguf or .safetensors. A model’s license is separate from the runner’s: check the license for the specific model you choose, since terms and degrees of openness vary. LM Studio explains these distinctions in its getting-started guide.
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Check whether your computer is a good fit
Requirements vary with the runner, model, context size, and task. The following are LM Studio’s stated platform requirements and recommendations; they are not universal minimums for all local AI software or a guarantee that a particular model will run well.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
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| Platform | LM Studio guidance |
|---|---|
| Apple Silicon Mac | macOS 14.0 or newer; 16 GB or more RAM recommended. Macs with 8 GB may work with smaller models and modest context sizes. Intel Macs are not currently supported. |
| Windows | Supports x64 and Snapdragon X Elite ARM systems. x64 requires AVX2. At least 16 GB RAM and at least 4 GB dedicated VRAM are recommended. |
| Linux | Supports x64 and ARM64; distributed as an AppImage. Ubuntu 20.04 or newer is listed. |
These details come from LM Studio’s system requirements; the page does not state a publication year for its recommendations. Ollama notes that speed depends on hardware and that large models can be slow without a strong GPU. If memory or speed is limited, begin with a smaller model and test the task you actually want to do rather than assuming a given computer can handle every model.
Choose a runner and install it
LM Studio: guided desktop setup
LM Studio provides a graphical app for finding, downloading, and loading models. Its documented setup is geared toward using the app itself for chat, so a separate interface is not required.
- Check the platform guidance above, then download and install the current LM Studio app for your system.
- Open Discover, search for a model, and download one whose requirements and license suit your computer and intended use.
- Open the Chat tab and use its model loader to load the downloaded model. Loading allocates memory for the weights and other parameters.
- When the model has loaded, enter a simple prompt and check whether the response time and quality are adequate for your task.
LM Studio describes this workflow in its getting-started documentation.
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Ollama: command-line setup
Ollama’s official download page provides these installation commands for macOS or Linux and Windows PowerShell, respectively:
- macOS or Linux:
curl -fsSL https://ollama.com/install.sh | sh - Windows PowerShell:
irm https://ollama.com/install.ps1 | iex
Use Ollama’s download page for its current instructions and model-running details. Pay attention to whether a model is running locally or is an Ollama-hosted cloud model; those are not the same setup or privacy choice.
Pick a model, then test your real task
There is no universally best model or performance winner established for all computers. Choose according to your available memory, hardware, and intended work. If a model loads slowly or strains your system, try a smaller one. Test with representative prompts and documents, and judge response quality and speed for yourself; do not assume a local model matches a particular hosted service.
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Before using a model beyond experimentation, review its own license. A model being described as open-weight does not automatically mean it is open source or that all models carry the same usage terms.
Do you need another chat interface?
No. You can start in LM Studio or use the interface provided by your runner. Open WebUI is an optional interface that can connect to local model servers such as Ollama and also to hosted APIs. The endpoint selected for a conversation determines where inference happens: choosing a hosted endpoint means the prompt is sent to that provider. Comparing local and hosted models can send the same prompt to each endpoint you select.
Open WebUI also supports cloud-based tools and services for tasks such as extraction or embeddings. Selecting a local model does not make separately configured cloud services local. If you want Open WebUI’s additional features, consult its Quick Start and provider connection guide. Its documentation describes different container images and feature sets; beginners who only want a local chat can avoid adding Docker and another interface until they have a reason to use them.
Rank #4
- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
Does a local AI assistant work offline and keep data private?
Once model files are downloaded, local inference can work without an internet connection, but setup and some features use the network. LM Studio says that prompts entered while chatting with LLMs in LM Studio do not leave the device and that documents used for chat or retrieval-augmented generation remain on the machine and are processed locally. These are LM Studio’s vendor claims about its local operation, not a guarantee about every connected tool or endpoint.
LM Studio documents internet use for searching and downloading models, downloading runtimes, retrieving model catalog details, and checking for app updates. Its offline operation documentation says a downloaded model can then run entirely offline.
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Ollama says in its FAQ, “We don’t see your prompts or data when you run locally.” Ollama provides a local-only setting that disables its cloud features, including cloud models and web search. It binds to 127.0.0.1:11434 by default; changing the bind address can expose the service beyond the local interface, so do so only with appropriate security configuration.
Quick Recap
- For sensitive content, confirm that the selected model endpoint is local rather than hosted.
- Check whether document processing, search, embeddings, or other tools use separate cloud services.
- If offline operation matters, download the model and any required components first, then disconnect and test the workflow you intend to use.
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