Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsYou can experiment with a large language model on a computer you already own: choose a compatible local runner, download model weights, load them into memory, and try a few representative prompts. You do not need to buy a new computer first. The model’s size, your system’s memory and graphics hardware, and the runner’s platform support determine what will work well.
How do I run an LLM on my computer?
A local LLM setup has two parts: a runner, which is the software that loads and runs a model, and the model’s weights, which are files you obtain separately. The runner must support both your computer and the model’s format. LM Studio identifies GGUF and safetensors as common formats and cautions that model licenses and degrees of openness vary. See LM Studio’s getting-started guide.
For a first experiment, a graphical interface is often the simplest route. LM Studio’s documented flow is to find and download a model, select and load it into memory, then chat. Ollama offers an installer and a library of models in different sizes and task categories. If you want to connect a model to your own script or application, check whether the runner provides a local API or server.
1. Check your computer before choosing a model
Note your operating system, system memory (RAM), and graphics hardware. If you use Windows, check whether your graphics card has dedicated video memory (VRAM). On Apple Silicon, the system uses unified memory. Then compare your computer with the current requirements for the runner you plan to use.
#1 Best Overall
- 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 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, 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; 12% 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.
LM Studio’s undated requirements page, accessed in 2026, lists support for Apple Silicon Macs, Windows x64 and ARM, and Linux x64 and ARM64, subject to platform-specific requirements. It recommends macOS 14.0 or newer and 16 GB or more of RAM for Apple Silicon Macs; it also says that Macs with 8 GB may work with smaller models and modest context sizes. For Windows, LM Studio recommends at least 16 GB of RAM and at least 4 GB of dedicated VRAM. These are LM Studio’s recommendations, not universal minimums or guarantees for all runners and models. See LM Studio’s system requirements.
2. Choose a runner that fits your workflow
Choose based on how you want to experiment, not on a claim that one tool is universally best. LM Studio provides a graphical workflow for discovering, downloading, loading, and chatting with models, as well as local APIs. It documents support for llama.cpp models on Mac, Windows, and Linux, and MLX models on Apple Silicon. Ollama provides a local runner, a model library, and APIs. The llama.cpp project describes command-line chat and an OpenAI-compatible server in its official introduction. Platform and format compatibility should be checked for the specific combination you intend to use.
3. Pick a model that your hardware can load
Model libraries contain options with different sizes and purposes. Ollama’s library, for example, lists model families and variants as well as categories such as coding, vision, embeddings, and reasoning. Its Llama 3.1 listing includes 8B, 70B, and 405B parameter sizes; these are model specifications, not measures of quality or speed, and the listing can change. Browse the Ollama model library for current examples, then read the particular model’s card and license before using it.
Rank #2
- Built for Local AI Development: AMD Ryzen AI Halo is designed for local AI development and inference, featuring 128GB unified memory and support for up to 200B parameter models to build and run intensive AI workloads locally.
- 128GB Unified Memory: Features 128GB LPDDR5x unified memory at 8000 MT/s with 256 GB/s memory bandwidth, providing a shared memory pool across the CPU, GPU, and NPU to support larger AI models.
- AMD Ryzen AI Max+ 395 Processor: Features 16 cores, 32 threads, and Zen 5 architecture, paired with AMD Radeon 8060S integrated graphics featuring 40 RDNA 3.5 compute units and an AMD XDNA 2 NPU with up to 50 TOPS.
- Linux AI Developer Platform: Purpose-built for Linux-based AI development with full AMD ROCm software support and preloaded tools, models, and workflows optimized for local AI development.
- Compact, Connected Design: Includes a 2TB M.2 SSD, 10GbE LAN, Wi-Fi 7, Bluetooth 5.4, USB-C connectivity, and HDMI 2.1b.
Do not assume that a model described as “open weights” has one standard license or is unrestricted for every purpose. LM Studio warns that models differ in license and in how open they are. Confirm that the model’s terms suit your intended use and that its file format is supported by your chosen runner.
4. Download, load, and try representative prompts
- Install the runner. Follow its current official setup instructions for your operating system. Ollama’s installer and downloads are at ollama.com/download.
- Get the model files while connected. Use the runner’s model catalog or the model provider’s documented instructions. Check the model name and file variant before downloading.
- Load the model into memory. Use the runner’s load action or its documented command. A model must fit the resources available to the runner; try a smaller model or a more modest context setting if loading fails.
- Test the work you actually want to do. Try several prompts representative of your intended tasks, such as a short explanation, a summary of text, or a coding question if coding is your goal. Judge the responses for usefulness, not just whether the model starts.
- Keep a brief record. Note the model and version, file or quantization variant, runner version, computer, context setting, and observed response quality and latency. This helps you make a fair comparison if you try another model or change settings.
There is no supported universal speed or quality ranking for these options. Performance depends on the hardware and setup; compare models on your own computer with the tasks and settings that matter to you rather than treating parameter count as a benchmark.
What hardware do you need to run an LLM locally?
There is no single hardware threshold that applies to every runtime and model. The practical question is whether your computer can load the model you chose while leaving enough memory for its context and the rest of your system. More system memory, suitable graphics hardware, and a smaller model can all affect what is feasible, but the exact combination depends on the runner and model.
Rank #3
- 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.
Use the platform-specific figures above as LM Studio guidance only. They are not proof that every model will run well on a computer meeting those recommendations, nor do they establish what another runtime requires. Check the current requirements for your chosen runner and model before buying or upgrading hardware.
Try the computer you have first
If your existing computer can load a suitably small model, it may be enough to explore local inference. Start with a smaller model and modest context, then see whether the response quality and latency suit your needs. If it cannot load the model, first check compatibility, available memory, and the selected model variant before concluding that you need a different computer.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
When an upgrade may make sense
Consider a new computer or an upgrade only if your existing system cannot meet the requirements for the models and tasks you actually want. Compare supported operating system, system memory, GPU and dedicated VRAM (or Apple Silicon unified memory), model size, and context needs together. A computer with 16 GB of RAM is not a guarantee of useful speed or capacity for every model.
Rank #4
- 【Leading AI Mini Workstation】MINISFORUM AI MS-S1 Max Workstation comes with AMD Ryzen AI Max+ 395 processor, which uses AMD's latest generation Zen 5 architecture. It has 16 Cores and 32 Threads, the boost clock is up to 5.1GHz. The overall processor performance is up to 126 TOPS, and the NPU performance reaches up to 50 TOPS. AMD Ryzen AI enables improved productivity, advanced collaboration, and improved efficiency.
- 【AMD Radeon 8060S Graphics 】The MS-S1 Max Mini PC equipped with AMD Radeon 8060S Graphics which built on the new generation of RDNA 3.5 architecture AMD graphics, it brings ultra-high frame rate experiences and advanced content creation features anywhere and delivers staggering performance. It can handle all your computing and multimedia tasks efficiently.
- 【Five 8K Video Output】This MS-S1 Max Workstation comes with five video outputs, 1x HDMI (8K@60Hz), 2x USB4(40Gbps,Alt DP2.0,PD out 15W) and 2x USB4 V2(80Gbps,Alt DP2.0,PD out 15W) Outputs, which support multiple monitors display at the same time and provide a larger and wider filed of view and improve your work efficiency. It is used in fields that require high-performance computing and graphics processing, including digital signage and securities trading, as well as work that uses CAD, such as engineering design, scientific calculations, animation production, and post-production for movies and television
- 【 Fast and Stable Wire & Wireless Speed】It comes with Two 10G Lan Ports for wired connection and and Wi-Fi 7 / BT5.4 for wireless connection, which increased the network speed greatly and expand its functions and improved performance of computer to a large extent and allows you to use more networks such as software routers (OpenWRT / DD-WRT / Tomato etc.), firewalls, NAT, network isolation etc.
- 【Large Storage & Flexible Expandability】This Workstation equipped with 64GB LPDDR5-8000MHz + 2TB M.2 2280 PCIe4.0 SSD. There is another PCIe4.0 SSD slot available for up to 8TB, these SSD slots are compatible with RAID0 and RAID1, you can store movies, videos, photos, important files easily. What’s more, it also comes with 1x standard PCIex16 slot(PCIe4.0x4) inside.
What does “running locally” mean for internet access and privacy?
Once the needed model files are on your computer, inference can work without an internet connection. LM Studio says it can operate entirely offline after model files are obtained; it also says that chatting with downloaded models, chatting with documents, and running a local server do not require internet. Model search, downloads, runtime downloads, and update checks can require connectivity. See LM Studio’s offline-operation documentation.
Local inference is not the same as never connecting to a network. In particular, a local server may be reachable on your local network, so offline operation alone does not determine who can access it. Check the runner’s server and network settings before enabling a server or connecting another device. LM Studio says local chat inputs stay on the device; treat that as the vendor’s statement about its own software, rather than a blanket privacy guarantee for every runner, model, or connected feature.
Which local LLM setup should you choose?
There is no universal winner. Match the tool to your workflow and verify the details that affect your setup:
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Setup style: Choose a GUI for visual model discovery and chat, a runner or CLI flow if you prefer commands, or a lower-level inference engine if you need more direct control.
- Platform and file format: Confirm support for your operating system, hardware, and the particular model format.
- Hardware fit: Account for system RAM, dedicated VRAM or unified memory, model size, and context setting.
- Next steps: Decide whether standalone chat is enough or whether you need a local server or API for scripts and applications.
- Network exposure: Distinguish downloading or searching for models from inference on files already stored locally; check server settings separately.
- License: Read the selected model’s terms for your intended use rather than assuming “open” means unrestricted.
For current details, consult LM Studio’s documentation, Ollama’s download page and model library, or the llama.cpp project introduction. Their supported platforms, requirements, and listings can change.
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