Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteTo run a large language model (LLM) on your computer, install a local model runner, download compatible model weights, load a model that fits your available memory, and start chatting. For a graphical setup, LM Studio provides a download-and-chat workflow; Ollama offers desktop apps, command-line access, and a local API. Your operating system, RAM, GPU, storage, and chosen model determine what will work well.
What you need to run a local LLM
A local setup has two essential parts: a runner that executes the model and the model’s weights, which are the files containing what it learned. Common weight formats include GGUF and Safetensors. Model licenses and how open a model is vary, so check the license for the specific model you download. LM Studio’s getting-started guide describes these formats and the basic setup.
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Memory is a key constraint. Loading a model requires memory for its weights and other parameters; the amount available, model size, context size, and desired speed all affect whether it is practical. Published system recommendations are starting points, not a guarantee that every model will run smoothly on a computer that meets them.
Also allow for disk space. Ollama’s Windows documentation says model files can take tens to hundreds of gigabytes. More storage lets you keep larger or more models; it does not, by itself, make responses faster. Ollama’s Windows documentation explains how to change the model storage location.
#1 Best Overall
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Choose a setup path
| Option | Best suited to | What it offers |
|---|---|---|
| LM Studio | People who prefer a graphical app | Discover and download models, load one into memory, and chat through the app. |
| Ollama | People who want desktop installation with command-line or API access | Install for macOS, Linux, or Windows; run models locally and access a local API. |
| llama.cpp | People comfortable with a lower-level, command-line-oriented route | Its official documentation describes GGUF model files, a command-line interface, and an OpenAI-compatible server. |
There is no controlled performance comparison in the cited project documentation establishing that one of these options is universally faster. Ollama says, “Speed depends on the hardware.” Choose based on your system, preferred interface, model format, offline needs, and whether you need an API.
Graphical setup with LM Studio
- Check compatibility. Compare your operating system and hardware with LM Studio’s system requirements.
- Install LM Studio. Download and install the application for your system.
- Find and download a model. Open the app’s Discover tab, select a compatible model, and download it. Review its license as well as its memory requirements.
- Load the model. Open the model loader and load the downloaded model into memory. Loading is distinct from downloading: the model must be available in memory to chat with it.
- Start a chat. Once the model has loaded, use the chat interface to send a prompt.
These steps follow the sequence in LM Studio’s getting-started guide. If a model will not load or responds too slowly, try a smaller model or a more modest context size rather than assuming your computer can run every model in the catalog.
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Install and use Ollama
Ollama provides installers for macOS, Linux, and Windows. Its download page lists the available options and, for macOS/Linux, gives the command curl -fsSL https://ollama.com/install.sh | sh; for Windows PowerShell it gives irm https://ollama.com/install.ps1 | iex. Follow the current instructions for your operating system, then use Ollama’s documented model workflow to obtain and run a model.
On Windows, Ollama’s documentation specifies Windows 10 version 22H2 or newer. It describes NVIDIA and AMD driver support for GPU acceleration and says the local API is served at http://localhost:11434. Those Windows-specific details are documented in Ollama’s Windows guide; do not assume they apply unchanged to other operating systems.
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If you need to move model files to another drive on Windows, Ollama documents the OLLAMA_MODELS environment variable for changing the model location. This can help with capacity management, but relocating files to an external SSD is a storage choice, not a performance upgrade by itself.
Check whether your computer is a suitable fit
LM Studio’s currently published guidance is platform-specific. It is product documentation, not an independent benchmark or a universal minimum for every local runner.
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.
| Platform | LM Studio documentation | How to interpret it |
|---|---|---|
| Apple Silicon Mac | M1, M2, M3, or M4; macOS 14 or newer; 16 GB or more RAM recommended. Smaller models and modest context sizes may work on 8 GB Macs. | The 8 GB statement is qualified: it is for smaller models and modest context sizes, not a promise of good performance for all models. |
| Windows | x64 and ARM systems are supported; x64 requires AVX2. At least 16 GB RAM and 4 GB dedicated VRAM are recommended. | These are LM Studio’s recommendations, not a guarantee that any particular model or speed target will be met. |
| Linux | x64 and ARM64 support; Ubuntu 20.04 or newer. The documentation describes newer Ubuntu versions as less well tested. | Check the current requirements for your distribution and hardware before installing. |
See LM Studio’s system-requirements page for its current platform notes. For a realistic model choice, consider RAM and GPU memory alongside the model’s requirements, the context size you plan to use, and how quickly you expect answers.
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Can you use a local LLM offline?
With LM Studio, the documented answer is yes once the model files are on your computer. LM Studio says it can operate entirely offline and that local chat, document processing (including retrieval-augmented generation), and local-server requests do not require internet access. Its documentation also says chat entries and documents stay on the device. LM Studio’s offline guide describes those behaviors.
You still need connectivity to search for or download models and runtimes, and some catalog or update functions require a network connection. These offline and data-handling statements are specific to LM Studio’s documented functionality; they are not a blanket guarantee about every local-model app, plugin, telemetry setting, or network configuration.
Quick Recap
Common setup problems and what to check
- The model will not load: Check available memory and try a smaller model or a more modest context size.
- Responses are too slow: Performance depends on hardware and model choice. The reviewed documentation does not provide a controlled speed ranking of these runners.
- The download fails or model search is unavailable: Model discovery and downloads require an internet connection; offline use applies after needed files are present.
- Your drive is running out of space: Model files can be large. On Windows, consult Ollama’s documentation for the
OLLAMA_MODELSsetting to relocate its model store. - A Windows GPU is not being used: For Ollama, check that the system meets its Windows version requirement and that the relevant NVIDIA or AMD driver support is in place.
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