Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsYou can try a language model on your own computer without buying a new machine first. Install a local runner, download compatible model files, load them into memory, and test them with a few prompts. The model files and the software that runs them are separate; your computer’s memory and graphics hardware help determine which models are practical.
How do I run an LLM on my computer?
A local LLM setup has two parts: a runner, the software that loads and runs the model, and the model’s weights, the files containing the model. The runner must support the model’s file format and your operating system and hardware. LM Studio identifies GGUF and safetensors as common formats and explains its basic workflow in its getting-started documentation.
- Check your computer. Note its operating system, system memory (RAM), and graphics hardware, including dedicated video memory (VRAM) if it has a separate graphics card.
- Choose a runner. For a graphical workflow, LM Studio offers model discovery, downloads, loading, and chat. Ollama offers an installer and a model library. For a lower-level command-line route, llama.cpp presents CLI chat and a server option; its surfaced documentation describes an OpenAI-compatible server. Choose based on workflow and compatibility, not an assumed speed advantage.
- Confirm requirements and compatibility. Check the runner’s current platform requirements and make sure it supports the model’s format. LM Studio documents llama.cpp models on Mac, Windows, and Linux, and MLX models on Apple Silicon; see its documentation overview.
- Choose a model and review its terms. Use the model’s own description and license to assess whether it suits your task and intended use. A library entry or an “open weights” label is not a substitute for checking those terms.
- Download and load the model. Download its files while connected to the internet, then use the runner’s interface or command-line workflow to load the model into memory. LM Studio documents a download, load, and chat flow; Ollama provides its own install and library flow at Ollama’s download page and model library.
- Try representative prompts. Test the kinds of questions or tasks you actually expect to use, rather than relying on a single demonstration prompt.
For a useful comparison between models on your own computer, record the model name and version, file variant or quantization, runner version, machine, context setting, and observed response quality and latency. This makes clear what you tried without treating an informal trial as a benchmark.
What hardware do you need to run an LLM locally?
There is no single hardware threshold that applies to every runner and model. Model size and context setting affect resource needs, and system RAM, dedicated VRAM, or Apple Silicon unified memory can constrain what your computer can load. Check the requirements for the runtime you select instead of treating a general-purpose specification as a guarantee.
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- 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 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
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LM Studio’s current, undated system-requirements page recommends 16 GB or more of RAM for Apple Silicon Macs and at least 16 GB of RAM plus at least 4 GB of dedicated VRAM for Windows PCs. It notes that Macs with 8 GB of memory may work with smaller models and modest context sizes. These are LM Studio’s platform-specific recommendations, not universal requirements or a promise of a particular speed. Its page lists support for Apple Silicon Macs, Windows x64 and ARM, and Linux x64 and ARM64; requirements and support can change.
Try your existing computer with a suitably small model before upgrading. If you are comparing machines, consider the supported operating system, system memory, GPU and VRAM, and the model size and context you want to use. A 16 GB laptop matches LM Studio’s memory recommendation for Apple Silicon Macs and Windows PCs, but that figure alone does not establish that every model will fit or run at a useful speed.
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Which local runner should you try?
| Option | What the official material describes | Useful when |
|---|---|---|
| LM Studio | GUI for finding, downloading, loading, and chatting with models; local APIs; documented support for llama.cpp models on Mac, Windows, and Linux and MLX models on Apple Silicon. | You want a graphical way to explore models or use a local API. |
| Ollama | Installer and library listing models in different sizes and task categories. | You want to run models through Ollama’s own install and library workflow. |
| llama.cpp | Its official project introduction presents command-line chat and a server option, including an OpenAI-compatible server. | You want a lower-level CLI or server workflow and are prepared to work with that setup. |
These descriptions are not a performance comparison: the available documentation does not establish a universal winner. Ollama’s library, for example, has listed Llama 3.1 in 8B, 70B, and 405B parameter sizes, alongside categories such as coding, vision, embeddings, and reasoning. Library contents can change, and a listing is not an independent quality or speed evaluation.
What does “offline” mean for a local LLM?
After the model files are on your computer, inference—the process of generating a response—can work without an internet connection. LM Studio states in its offline-operation documentation, “LM Studio can operate entirely offline, just make sure to get some model files first.” It says chatting with downloaded models, chatting with documents, and running a local server do not require internet.
Rank #3
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Some setup and discovery tasks do require connectivity: model searches, downloads, runtime downloads, and update checks can need an internet connection. Local inference also does not by itself determine whether a local server is reachable from other devices on your network. Check the server’s network settings if you enable one.
LM Studio says local chat inputs stay on the device. That vendor statement concerns its local operation; do not treat the word “local” as a blanket privacy guarantee for every runner, configuration, connected feature, or server exposure. Review the software and network settings you actually use.
Rank #4
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- MINI PC COMPUTER SUPPORTS QUAD SCREEN 8K DISPLAY - Nucbox M7 Ultra gaming pc is equipped with Dual USB4 USB-C Video output. The latest HDMI 2.1 port can connect to large screen TV and Display Monitors and output up to 8K@60Hz resolution. The Type-C DisplayPort Video output can connect to the latest monitor displays utilizing 4K@144Hz. Features simultaneous four screen display
- OCULINK PORT - The M7 Ultra Oculink port enables higher bandwidth capabilities, better frame rates and lower lag. The standard also operates at PCIe x4 speeds, compared to Thunderbolt's x3. Gamers and content creators can benefit from OCuLink's higher bandwidth, resulting in better performance and lower lag for eGPU setups
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Do local models have the same license?
No. LM Studio warns that models differ in their licenses and in how open they are. Read the specific model’s current terms before using it, especially if your intended use has legal or commercial requirements. Neither a local download nor the phrase “open weights” establishes that every use is permitted.
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What should you try first?
- Start with the computer you already own, and check the selected runner’s current requirements.
- Pick a model whose format and size are compatible with that runner and a reasonable fit for your available memory.
- Test prompts that reflect your intended tasks; note the model variant, runtime, machine, and context setting so you can interpret what you observe.
- Consider an upgrade only if your existing system cannot support the model or workflow you want.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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