To run an open-weights AI model on your computer, download its weights, choose an inference app that supports the model’s format, make sure your computer can handle the model and settings, then load it and test it with a short prompt. Start with a model that explicitly documents a local runtime; downloadable weights alone do not guarantee that a model will work on your system or permit every use.
What you need to run an AI model locally
A local model session has four essentials: model weight files, a compatible inference runtime, sufficient system resources for the chosen configuration, and a prompt to verify generation. Hugging Face’s Use AI Models Locally documentation walks through choosing a supported model, selecting an app from its model page, and running the supplied command. As Hugging Face puts it, “Your hardware is the limiting factor, not the server or connection speed.”
- Weights: The files that contain the model’s learned parameters. Common formats include GGUF and safetensors, but the exact format depends on the model and runtime.
- Runtime: Software that loads the weights and generates responses, such as LM Studio, Ollama, llama.cpp, or Jan.
- Resources: Memory, storage, and compatible hardware acceleration where available. The required amount depends on the model and its settings.
- First prompt: A short, simple request that confirms the model loaded and can generate a response.
How to choose a model and a local runner
Check the model card first
Choose the model before choosing an app. On the model’s page, check the variant, available weight files, supported runtimes or example commands, and the license and usage policy. “Open-weight” describes access to weights; it does not mean every model release has identical terms. For example, OpenAI says its gpt-oss weights are under Apache 2.0, subject to its usage policy, and can be run with common open inference stacks including vLLM, Ollama, and llama.cpp. See OpenAI’s gpt-oss overview for the models and setup guidance.
Match the runtime to your workflow
There is no single best runner for every computer and use case. Hugging Face lists several options in its local-app documentation:
#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: A graphical app with a model browser, download flow, model loader, chat, and developer APIs.
- Jan: An offline graphical app with document-chat features and an API server.
- Ollama: A command-line app with Hugging Face Hub integration.
- llama.cpp: A C/C++ runtime with command-line, server, and Python interfaces, and support for CPUs, CUDA, and Metal.
Before installing, verify that the runtime supports the model’s exact file format and that its documented operating-system, processor, and acceleration support fits your machine. A model’s parameter count by itself is not enough to predict whether it will fit or run well: quantization, context length, runtime overhead, and other applications also affect resource use.
Check whether your computer is suitable
Requirements vary by app, model, and configuration; the guidance for one runtime is not a universal minimum for all local inference. LM Studio’s system requirements, reviewed in 2026, provide one app-specific reference:
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.
- Apple Silicon Mac: LM Studio supports M1, M2, M3, and M4 with macOS 14.0 or newer. It recommends at least 16 GB of RAM; 8 GB Macs may still work with smaller models and modest context.
- Windows: LM Studio supports x64 and ARM systems using Snapdragon X Elite. Its x64 support requires AVX2. It recommends at least 16 GB of RAM and 4 GB of dedicated VRAM.
- Linux: LM Studio documents x64 and ARM64 support and distributes an AppImage. It lists Ubuntu 20.04 or newer, while noting versions newer than Ubuntu 22 are not well tested.
These are LM Studio’s published support recommendations, not requirements for Ollama, llama.cpp, Jan, or every model. Check the selected runtime’s current requirements and the model card’s recommended format and settings before downloading. The reviewed sources do not establish a universal RAM formula that maps a parameter count to a guaranteed fit.
Install, download, and run a first prompt
Graphical setup with LM Studio
- Install LM Studio for a supported operating system and architecture, using its current documentation and system requirements.
- Open Discover, find a model, and check its format and model-page instructions before downloading. LM Studio documents support for weight files such as GGUF and safetensors.
- Open the model loader, select the downloaded model, and optionally adjust the load parameters. Loading a model allocates memory for its weights and other parameters.
- Start a chat and try a small prompt, such as: “In two sentences, explain what a local language model is.” Confirm that a response appears before trying longer prompts or more demanding settings.
LM Studio’s getting-started documentation describes the install, Discover, load, and chat flow. It notes that “Loading a model typically means allocating memory to be able to accommodate the model’s weights and other parameters in your computer’s RAM.”
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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.
Command-line setup with a model-page command
- Open the chosen model’s page on Hugging Face Hub and confirm that it documents a compatible local app and format.
- Select Use this model, choose an app such as Ollama or llama.cpp, and review the command supplied for that model.
- Install the runtime using its official instructions, then run the model-page command in a terminal.
- Send a short prompt first. If the runtime exposes a local server or API and your goal is to connect another application, verify a basic response before adding integrations.
Using the command shown for the specific model helps avoid assuming that all models use the same command or format. Hugging Face’s documentation also describes llama.cpp’s CLI, server, and Python interfaces, and Ollama’s Hub integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to expect from local use
Local inference can keep prompts on infrastructure you control, but it is not automatically private in every setup. Downloading the weights requires an initial connection, and optional integrations, telemetry, or remote services may send data elsewhere; review those features separately. OpenAI says it does not receive data sent to self-hosted gpt-oss unless users share it or use a managed hosting partner, and that local or self-hosted costs vary and may or may not be lower than API costs once operations are included. Its gpt-oss overview describes these models as designed to run on infrastructure users control.
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.
“Free weights” also do not make inference cost-free: your computer supplies the compute, storage, and electricity, and you maintain the software and files. A model can produce incorrect or unsafe output whether it runs locally or remotely, so evaluate responses for your task and review them before relying on them.
Troubleshoot a failed or slow first run
- The model will not load: Confirm that the runtime supports the file format, that the download completed, and that your system meets the runtime’s requirements. Close memory-heavy applications and retry.
- You run out of memory: Try a smaller model, a supported quantized file if available, or a shorter context setting. Weight size is only part of the memory demand, so reducing context or competing application use can also help.
- Generation is very slow: Check whether the runtime is using a supported acceleration backend for your hardware. Consider a smaller model or quantized variant. Without measurements on your exact machine and configuration, no reliable speed can be promised.
- The model page gives a different setup: Follow the model-specific instructions and check for a compatible runtime rather than substituting a generic command.
Downloaded weights can occupy substantial storage, but an external SSD is optional, not a requirement. If you use one to keep model files separate from internal storage, check the actual file sizes and connection needs for the models you plan to download.
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