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You can run an open-weight AI model on hardware you control and chat with it through a self-hosted interface. A practical starting point is Ollama for running models and Open WebUI for the chat experience. When a request goes to a local model, its prompt need not be sent to a hosted inference API—but privacy depends on which endpoint and extra services you configure. Local use also replaces some API charges with hardware, electricity, storage, and maintenance costs.
How a local AI stack works
The chat interface and the model runtime are separate parts. Open WebUI provides the interface; a provider endpoint receives the request and performs inference. Ollama is one option for local inference. Open WebUI also documents connections to llama.cpp, vLLM, and compatible hosted providers. See Open WebUI’s provider connection guide.
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In a local setup, the general request path is your browser or app, then Open WebUI, then the local runtime and model. The endpoint matters: Open WebUI cautions that “The selected endpoint determines where inference happens.” If you select a hosted model, the prompt and the context included with it go to that provider instead.
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- Ollama: Runs compatible models locally and exposes a service that another application can connect to.
- Open WebUI: Supplies a self-hosted chat interface and can connect to local runtimes or hosted providers.
This pairing is a starter architecture, not a requirement. You can use another compatible local server, particularly if your workload calls for a different serving setup.
#1 Best Overall
- 【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
Set up Ollama and Open WebUI
Choose the workload before choosing a model or buying hardware. Private document questions, occasional drafting, coding, and serving several people at once can call for different models, context sizes, and response speeds. There is no universal model-and-hardware pairing established here; check current model compatibility and the runtime’s hardware guidance for your intended system.
- Install a local model runtime. Install Ollama on the machine that will run inference, then obtain a model compatible with that runtime and your hardware. Ollama’s official site provides its current downloads and documentation.
- Install Open WebUI. Follow the Open WebUI quick-start guide for your operating system or container setup.
- Connect the interface to the runtime. Configure Open WebUI to use your local Ollama endpoint, following its provider connection instructions. Check that the selected provider for a conversation is the local one.
- Keep Open WebUI’s data persistent if using containers. The quick-start documentation explains how to persist its data volume and set a secret key. Without persistent storage, container replacement can put saved application data at risk.
- Configure hardware access deliberately. If you use containers, GPU access must be configured for the container that needs it. Open WebUI’s CUDA image accelerates Open WebUI’s own embedding, reranking, and speech components; it does not automatically grant a separate Ollama container access to the GPU.
- Test with a representative task. Try the actual kind of prompt or document you plan to use, at the context length you expect. Check that responses are usable and the machine has adequate memory and speed before relying on the setup.
What “private” means—and what it does not
Ollama says in its FAQ, “We don’t see your prompts or data when you run locally.” That is a statement from Ollama about its local runtime, not an independent audit of every part of a self-hosted installation. Whether a request remains local depends on the endpoint you select and any separately configured tools or services.
Rank #2
- Built for Local AI and Advanced Workflows – The BOSGAME M5 AI Mini PC is powered by AMD Ryzen AI Max+ 395 with 16 cores, 32 threads, up to 5.1GHz, 50 TOPS NPU performance and up to 126 TOPS total AI performance. It is designed for local AI inference, private AI assistants, coding, data analysis, virtualization, content creation and demanding multitasking while keeping sensitive data on the device.
- 128GB Unified Memory for Large Models and Creative Projects – M5 includes 128GB LPDDR5X-8000 unified memory, giving the CPU and Radeon 8060S graphics access to a large shared memory pool. This helps support memory-intensive AI workloads, large project files, multiple virtual machines, 3D work, video editing and complex professional applications without the capacity limits of typical 32GB or 64GB mini computers.
- Radeon 8060S Graphics for Creation, Rendering and Gaming – Integrated Radeon 8060S graphics with 40 RDNA 3.5 compute units delivers high-end visual performance without a separate graphics card. Use the M5 creator workstation for 4K video editing, 3D rendering, CAD, AI image workflows, high-resolution media and modern gaming, while maintaining a compact desktop footprint.
- 2TB PCIe 4.0 SSD and Flexible Expansion – A pre-installed 2TB NVMe PCIe 4.0 SSD provides fast access to models, datasets, media libraries and project files. A second M.2 2280 PCIe 4.0 slot allows additional storage expansion, while the SD 4.0 card reader supports efficient photo and video workflows for creators and production teams.
- Professional Connectivity and Four-Display Support – Dual USB4 ports, HDMI 2.1 and DisplayPort 1.4 support up to four displays and resolutions up to 8K@60Hz. WiFi 7, Bluetooth 5.4 and 2.5GbE deliver fast networking for cloud collaboration, NAS access and business deployment. Windows 11 Pro, performance-mode switching, Wake-on-LAN and auto power-on support flexible workstation use.
- Check tools and integrations individually. Web search, cloud tools, document extraction, embeddings, and other services can send data elsewhere even when the chat model itself is local. A local model does not make those components local by default.
- Decide whether Ollama cloud features are needed. Ollama documents a local-only option for disabling its cloud features. Doing so also removes access to Ollama’s cloud models and web search.
- Keep network access intentional. Ollama’s server binds to
127.0.0.1:11434by default. Changing the bind setting can make it reachable on a network. Keep it on loopback unless remote access is needed; if you expose a service, secure and manage that access deliberately. - Remember that local does not mean isolated. A network-accessible server or a cloud-connected integration changes the privacy boundary, even if the model files are stored on your computer.
For an air-gapped or offline goal, review every configured provider and integration, not only the chat model. Open WebUI addresses offline use in its documentation and FAQ; actual offline behavior depends on the services and components enabled in your deployment.
Choose hardware for the model and workload
There is no single GPU or memory figure that fits every local AI setup. Model size, quantization, context length, available VRAM and system RAM, GPU compatibility, and other software running at the same time all affect whether a workload fits. Check the compatibility information for the runtime and model before spending money.
Rank #3
- 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.
Memory and context length
Longer context lets a model consider more input, but consumes more memory. Open WebUI’s current context guide reports these defaults for Ollama v0.15.5, based on available VRAM:
| Available VRAM | Reported default context length |
|---|---|
| Below 24 GiB | 4,096 tokens |
| 24 to 48 GiB | 32,768 tokens |
| 48 GiB and above | 262,144 tokens |
These are version-specific defaults reported in Open WebUI’s documentation, not universal recommendations or a guarantee that a particular model can practically use that context. Larger context requires more VRAM and RAM, so set a length your model and system can handle rather than treating the default as a target.
Rank #4
- 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.
What to evaluate before upgrading
- Whether your intended runtime supports the GPU and can use it in your operating system or container setup.
- Whether available VRAM and system RAM suit the specific model, quantization, and context you intend to run.
- Whether the system has enough SSD storage for model files and application data.
- Whether the machine’s power use, noise, and maintenance are acceptable for how often you will use it.
- Whether you need a personal workstation or a server for concurrent users. Open WebUI lists vLLM as a local high-throughput option, but that alone does not establish a performance advantage for a particular workload.
Local inference versus hosted APIs
Moving suitable requests to local models can reduce hosted inference charges for those requests. It does not establish a guaranteed saving: local inference shifts costs to hardware, electricity, storage, and upkeep. Ollama also offers hosted plans, so its locally run software and its hosted services should not be treated as the same thing. No universal break-even point or comparable capability benchmark is established here.
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| Consideration | Local model on your hardware | Hosted API |
|---|---|---|
| Where inference happens | On the endpoint you run and select; extra integrations may still be remote. | At the selected provider, which receives the prompt and included context. |
| Inference charges | Can avoid per-request hosted inference charges for work moved to local models; hardware and operating costs remain. | Charges and terms depend on the provider and plan. |
| Hardware and upkeep | You supply compatible hardware and manage installation, storage, power, and maintenance. | No local inference machine is required, though you still need a device to access the service. |
| Capability | Depends on the chosen model and how it performs on your actual task. | Depends on the provider and model selected. |
| Concurrency | Depends on your server and configuration; high-throughput local serving may call for a different runtime. | Depends on the service and plan. |
Compare options with your own workload: test the tasks that matter, note response quality and speed, and account for the cost of hardware you already own or would need to buy. This avoids assuming local models match premium hosted models or that every API request is worth moving onto a local machine.
Quick Recap
Best Value
- 【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.
A practical decision checklist
- Choose local when controlling the inference endpoint and keeping selected prompts off hosted inference services matter, and your machine can run a suitable model.
- Choose hosted when you prefer not to manage inference hardware and are comfortable sending prompts and included context to the provider you select.
- Use a mixed setup when some work belongs on a local model and other work benefits from a hosted service. Verify the endpoint conversation by conversation and inspect integrations separately.
- Delay a hardware purchase until you have identified a model, context length, and workload, then checked compatibility and memory requirements against the system you own.
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