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You can run an AI model on your computer by installing a local model runner, downloading compatible model weights, loading them into memory, and chatting through a desktop app or command line. That keeps inference on your device, but it does not automatically make every feature in the app offline: check integrations and network settings, and verify the exact workflow if offline use is essential.
What “running locally” means for privacy
In local inference, your computer performs the model’s calculations using weights stored on the device. You do not need to send each prompt to a hosted inference service for that local chat to work. However, an application may also offer cloud-backed integrations, network-accessible APIs, or other connected features. A local model runner is not, by itself, an end-to-end privacy guarantee.
Ollama documents a local API endpoint, and LM Studio documents local and network API endpoints. An API can make a model available to other software or devices; keep it bound to loopback unless you deliberately need remote access and have secured it. The documentation describes endpoint capabilities, not an independent security audit. See Ollama’s quickstart and LM Studio’s API endpoint documentation.
How to check an offline requirement
- Download the installer and model weights from their official sources before going offline.
- Review the runner’s settings and documentation for the specific chat or integration you plan to use; do not assume every feature shares local chat’s data path.
- If the requirement is that the workflow function without a network connection, test that intended workflow with networking disabled. This is a verification step, not a guarantee about every application feature.
- Do not expose a local API to your network unless you intentionally need that access and understand how it is secured.
Choose a local model runner
| Tool | Good fit | What it supports |
|---|---|---|
| LM Studio | People who prefer a graphical interface | Find and download models, load them, and chat in the app; its documentation also covers local and network API endpoints. |
| Ollama | People comfortable with a short command or who want a local API | Run models from the command line, manage downloads, inspect loaded models, and use a locally served REST API. |
| llama.cpp | People who want lower-level control or hardware-backend options | Run GGUF model files with multiple CPU and GPU backends, quantization options, and possible hybrid CPU/GPU inference. |
These are different workflows rather than a universal ranking: LM Studio emphasizes a GUI, Ollama a concise CLI and API, and llama.cpp more direct control over inference and hardware backends. Details and model compatibility can change; consult the projects’ documentation: LM Studio documentation, Ollama quickstart, and llama.cpp README.
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- 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.
Set up a local model
Option 1: Use LM Studio’s graphical interface
- Check LM Studio’s system requirements for your operating system and hardware.
- Install LM Studio and open the Discover tab to find and download a model. Check the exact model’s license and permitted uses rather than choosing by model name alone.
- Open the model loader, select the downloaded model (or a compatible model you sideloaded), and load it. Loading allocates memory for weights and other runtime parameters.
- Start a chat. If keeping prompts local is important, review the settings and documentation for any integration or connected feature you use.
Option 2: Run a model with Ollama
- Install Ollama from its official download page for your operating system.
- Open a terminal and run
ollama run llama3.2. Ollama’s quickstart uses this as a minimal chat example; the model tag and catalog may change, so check the current listing if it is unavailable. - Use
ollama pullto download a model without starting a chat,ollama listto see downloaded models, andollama psto inspect models currently loaded. - Use the documented local REST API only if another local application needs it, and check its network exposure before relying on it for sensitive work.
On Windows, Ollama says the app runs natively and exposes its API at http://localhost:11434. Its Windows documentation says the binary install needs at least 4 GB of space, with model storage additional and potentially tens to hundreds of GB; OLLAMA_MODELS can change the model storage directory. See Ollama’s Windows documentation.
Option 3: Use llama.cpp for more control
llama.cpp requires GGUF model files. Its README describes obtaining compatible weights or converting other formats, then running inference through the project. It supports Apple silicon, x86 CPU instruction sets, NVIDIA CUDA, AMD HIP, Vulkan, SYCL, and other backends, along with quantization and hybrid CPU/GPU inference. That flexibility can help when a model exceeds available GPU memory, but performance and setup depend on the particular hardware, model, and configuration. Start with the project’s README for supported options and instructions.
Rank #2
- Unlock next-generation AI computing with AMD Ryzen AI Max+ 395 processor featuring 16 cores, 32 threads, up to 5.1GHz boost clock, and integrated Ryzen AI engine delivering up to 126 TOPS AI performance. EVO-X3 is designed for local AI models, content creation, development, and professional workloads.
- OCuLink External GPU Expansion – Upgrade Beyond a Mini PC: Take your graphics performance further with a dedicated OCuLink (PCIe 4.0 x4) interface. Connect an external GPU dock to add desktop-class graphics power for AAA gaming, AI acceleration, 3D rendering, video production, and advanced creative applications. EVO-X3 gives you the flexibility of a compact PC with workstation-level expansion capability.
- 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.
How much memory and storage do you need?
There is no single RAM figure that guarantees a particular model will run well. Model weights are only part of memory use: runtime state, configuration, context length, and concurrent work also matter. Depending on the system, inference may use system RAM, unified memory, GPU VRAM, or a combination.
Ollama’s quickstart lists the following model-library sizes and rough memory guidance. These are examples on that documentation page, accessed in 2026—not universal requirements, performance guarantees, or a current hardware buying recommendation.
Rank #3
- 【High-Performance APU】The MS-S1 MAX features an AMD Ryzen AI Max+ 395 APU, integrating a Zen 5 architecture CPU (up to 5.1GHz, 16C/32T, 64M L3 Cache), an RDNA 3.5 GPU, and an NPU (50 TOPS). The total system output is 126 TOPS. It provides powerful parallel computing capabilities for demanding AI workflows. It is ideal for running local LLMs, multimodal models, and computationally intensive tasks
- 【128GB UMA Memory】Equipped with up to 128GB of LPDDR5x-8000MT/s unified memory, it enables the CPU and GPU to access a shared, high-bandwidth memory pool with extremely low latency. Ideal for large-scale AI inference, 3D workloads, and complex timelines in video editing. It eliminates traditional VRAM bottlenecks, ensuring smoother data transfer during high-intensity computations. The UMA design maximizes performance stability under high loads
- 【Flexible Expansion】The MS-S1 MAX features USB4 V2 (up to 80Gbps), dual 10GbE LAN, HDMI 2.1 (up to 8K60), a full-length PCIe x16 expansion slot, and dual M.2 slots supporting up to 16TB RAID 0/1. Wi-Fi 7 provides stronger signal coverage and a more stable wireless experience. The slide-out design facilitates upgrades and maintenance. It easily adapts to personal, studio, or rack-mount enterprise environments
- 【High-Efficiency Cooling System】Utilizing an aerospace-grade aluminum alloy chassis, copper base plate, six heat pipes, dual turbine fans, and advanced PCM thermal conductive material, it maintains stable cooling performance even under continuous load. This system supports 130W continuous power and 160W peak power operation, with a built-in 320W power supply. It boasts multiple global certifications including CCC, FCC, UL, CE, and UKCA, ensuring stable and reliable operation in various environments
- 【Cluster Design】Two MS-S1 MAX units can be configured as a dual-unit cluster to run a large 235B Q4 model locally, achieving an output speed of 10.87 tok/s. Supporting 2U rack deployment, multiple MS-S1 MAX units can be cascaded into a distributed cluster to create a high-efficiency AI computing center. A cluster of four MS-S1 MAX units successfully ran a DeepSeek-R1 671B Q4 large model. A reserved cluster power-on interface allows for unified start-up and shutdown
| Ollama quickstart example | Listed download size or RAM guidance | Qualification |
|---|---|---|
| Llama 3.2 1B | 1.3 GB | Model-library size listed in Ollama’s quickstart, accessed in 2026. |
| Llama 3.2 3B | 2.0 GB | Model-library size listed in Ollama’s quickstart, accessed in 2026. |
| Llama 3.1 70B | 40 GB | Model-library size listed in Ollama’s quickstart, accessed in 2026. |
| Llama 3.1 405B | 231 GB | Model-library size listed in Ollama’s quickstart, accessed in 2026. |
| 7B models | At least 8 GB RAM | Ollama’s rough guidance; actual use depends on the model, quantization, context, and runtime. |
| 13B models | At least 16 GB RAM | Ollama’s rough guidance; actual use depends on the model, quantization, context, and runtime. |
| 33B models | At least 32 GB RAM | Ollama’s rough guidance; actual use depends on the model, quantization, context, and runtime. |
File size helps estimate disk space, not whether the model will fit in working memory or respond at a useful speed. Before downloading, consider the memory available to the runner, the model format and quantization, the context you need, and whether your hardware can use an appropriate acceleration backend. Model downloads can be large; Ollama’s Windows documentation says storage needs may reach tens to hundreds of GB. An external drive is an option only if internal space is insufficient and the runner can use that storage location.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the model’s license and format
“Open source” does not mean every model has the same license or use permissions. LM Studio’s documentation notes that models vary in their licenses and degree of openness. Read the license for the exact model, especially before commercial use or redistribution. LM Studio describes formats including GGUF and safetensors, and also supports MLX on Apple silicon; llama.cpp centers on GGUF. The runner’s supported formats and your chosen model must be compatible. See LM Studio’s documentation and the llama.cpp README.
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.
Choose a setup that fits your computer
- Prioritize simplicity: start with LM Studio if you want to download, load, and chat through a GUI.
- Prefer commands or local integrations: try Ollama’s CLI and local REST API, while checking which applications can reach that API.
- Need backend choices or more control: consider llama.cpp if you are comfortable selecting compatible GGUF files and configuring hardware support.
- Plan around memory and storage: choose a model that fits your machine’s available memory and disk, rather than relying on a model’s name or file size alone.
- Need a particular use permission: inspect the exact model license before adopting it.
No universal “best GPU” or reliable tokens-per-second estimate follows from the available documentation alone. Actual performance depends on the model, quantization, runtime settings, context, and the CPU/GPU or unified-memory system doing the work.
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