LLMs
Qwen3 32B, 70B-class quantized workflows, and DeepSeek-style reasoning experiments.
Local AI infrastructure for power users
Run Large AI Models Locally
Run 70B-class quantized models, DeepSeek-style reasoning workflows, AI agents, private RAG, image generation, and voice models on your own machine.
Own your AI workspaceNo recurring cloud GPU billsNo per-generation cloud limitsNo private data uploaded by default
Founding user program for AI power users, founders, agent builders, and small AI teams.
What you can run locally
One Local Machine. Multiple AI Workflows.
Personal AI Server is designed for local LLMs, agents, private knowledge bases, image generation, voice models, and multimodal experiments.
Agents
Local coding agents, tool-using agents, and long-running automation tasks.
Private RAG
Keep documents, prompts, datasets, and knowledge bases on hardware you control.
Multimodal
Image generation, voice workflows, vision models, and AI content experiments.
| Type | Recommended Models / Tools | MVP Status |
|---|---|---|
| LLM | Qwen3 32B | Testing |
| LLM | Qwen3 70B-class quantized workflows | Testing |
| Reasoning | DeepSeek distill / DeepSeek-style workflows | Planned validation |
| Agent | OpenHands, local coding agents | Planned validation |
| RAG | Open WebUI, private knowledge base | Testing |
| Image | Flux, SDXL | Testing |
| Voice | CosyVoice, GPT-SoVITS | Planned validation |
| Vision | Qwen-VL | Planned validation |
Model support depends on configuration, quantization, software version, and workflow settings. Verified test results will be shared with founding users before final purchase decisions.
Why memory matters
Built For Models That Ordinary PCs Cannot Fit
For local AI, memory is often the real bottleneck. Many AI machines are fast enough for demos but limited when you try to run larger models, long-context workflows, RAG systems, or multiple AI tools at the same time.
Personal AI Server is designed around large unified memory, giving local AI builders more room for large quantized models, private knowledge bases, agents, and multi-tool workflows.
128GB Unified Memory
- Run larger local model workflows
- Keep RAG, LLM, and agent tools in one environment
- Work with private documents and datasets locally
- Reduce dependence on cloud GPU rentals
Positioning
Local AI Infrastructure, Not Another Mini PC Listing
You are not buying a chip. You are building your own local AI infrastructure.
Generic Mini PC Store
- Hardware specs first
- You configure everything
- Generic PC positioning
- Competes on price
- Focuses on chip names
Personal AI Server
- AI workflows first
- Preloaded local AI stack
- Local AI infrastructure positioning
- Competes on AI capability and setup quality
- Focuses on model and workflow outcomes
Preloaded stack
Preloaded Local AI Stack
Skip the setup pain and start with a working local AI environment designed for power users. The final included software stack will be shaped by founding user interviews and validation results.
OllamaOpen WebUIComfyUIPinokioLocal model managementPrivate RAG workspaceAgent tool setupVoice workflows
Hardware support
Powered By Hardware Built For Local AI
Personal AI Server is planned around AMD Ryzen AI Max+ 395, a 128GB unified memory configuration, 2TB SSD storage, and integrated Radeon graphics.
The hardware is selected to support local LLMs, agents, RAG, image generation, voice models, and long-running personal AI workflows in a compact machine.
AMD Ryzen AI Max+ 395128GB unified memory configuration2TB SSDRadeon 8060S integrated graphicsCompact local AI server form factor
Audience
Built For AI Power Users
LocalLLaMA Power Users
Run larger local models and experiment with local inference without renting cloud GPUs for every workflow.
AI Founders
Prototype private AI products, agents, internal tools, and demos on your own machine.
Agent Developers
Run coding agents, tool-using agents, and local automation workflows in a dedicated local environment.
Small AI Teams
Create a shared local AI workspace for RAG, model testing, internal tools, and workflow experiments.
Wrong fit clarification
Not The Right Machine For Everyone
Personal AI Server is not designed to be the fastest possible Flux or SDXL box. If your only goal is maximum image generation speed with CUDA, TensorRT, or RTX 5090-class GPU performance, a dedicated NVIDIA workstation may be a better fit.
This product is designed for users who care more about large local models, unified memory, agents, private RAG, multi-workflow AI infrastructure, and owning their AI environment.
Founding User Program
Apply For Early Access
We are selecting early users who want to help shape a personal AI server for local LLMs, agents, RAG, image generation, voice workflows, and private AI infrastructure.
- Early access to configuration details
- Model and workflow validation updates
- Private demo opportunity
- Founding user pricing discussion
- Input on the preloaded AI stack
FAQ
Questions Power Users Ask First
Can it run 70B models locally?
It is designed for 70B-class quantized model workflows. Final support depends on model version, quantization, memory use, context length, and software stack, so validated configurations will be shared before final purchase decisions.
Why use 128GB unified memory instead of an RTX 5090 workstation?
If your main priority is maximum Flux or SDXL speed in CUDA and TensorRT workflows, an RTX 5090 workstation may be a better fit. Personal AI Server is designed for large local model workflows, private RAG, agents, and multi-tool local AI infrastructure.
Is this better than renting cloud GPUs?
Cloud GPUs are still useful for peak performance and occasional heavy jobs. Personal AI Server is for users who want an always-available local AI environment for daily work, private files, repeatable experiments, and lower dependence on recurring cloud GPU bills.
Can I run AI agents locally?
Agent workflows are a core validation target. The MVP positioning includes local coding agents, tool-using agents, and automation workflows, but exact supported setups will depend on the final preloaded stack and model validation results.
Can I build a private RAG system?
Yes, private RAG is a core use case. The planned stack includes local model tools, Open WebUI-style workflows, and private knowledge base support so documents and prompts can stay in your own environment by default.
Does it support image generation?
Image generation is a supported workflow, including Flux and SDXL-style workflows. However, Personal AI Server is not positioned as the fastest image-generation machine against dedicated NVIDIA GPU workstations.
Does it support voice models?
Voice workflows such as CosyVoice and GPT-SoVITS are planned for validation. Final recommended workflows will depend on test results, model versions, and the preloaded stack selected with founding users.
Do I need to configure everything myself?
The goal is to provide a preloaded local AI stack, workflow recipes, setup guidance, and optional onboarding for founding users. The exact setup package will be shaped by early access interviews.
Who should not apply?
This is probably not the right fit if you only want a cheap mini PC, a gaming PC, or the fastest CUDA image generation box. It is designed for AI power users who want local AI infrastructure.
What price range should I expect?
The expected range is USD 3,299-3,999 depending on final configuration, software stack, and support package. Founding user feedback will help shape the final offer.
Next batch
Build Your Own Local AI Infrastructure
Apply for the founding user program and help shape a personal AI server for large local models, AI agents, private RAG, image generation, voice workflows, and AI automation.