Turnkey AI Platform for Local LLM, RAG & AI Workstations
Run a full AI stack locally on day one. Deliver LLMs, RAG, automation, creative AI, and developer tooling in one pre-integrated platform — including optional PyTorch, TensorFlow, CUDA, RAPIDS, Triton, Docker, and workstation-tuned configs.
This turnkey AI platform is designed for organizations that want to run local LLMs, RAG pipelines, and production AI workloads on a high-performance AI workstation—without cloud dependency for core workflows.
Architecture deep-diveLocal vs cloudFAQ
Web Browser / VS Code / NoMachine / SSH
Who It's For
Built for teams that want local AI without infrastructure friction
AI Workloads
AI Workloads You Can Run (LLM, RAG, Automation & Creative AI)
Private AI Assistant
Run local LLM and chat workflows with Open WebUI, Ollama, vLLM, and compatible APIs.
Document Intelligence
Build secure RAG pipelines with local embeddings, vector search, and natural-language Q&A.
AI Automation
Connect local LLMs to event-driven workflows, webhooks, and internal processes.
Creative AI Studio
Generate images, video, and voice locally with ComfyUI, Fooocus, and GPT-SoVITS.
What Makes This Different
Not a software install. A conflict-resolved AI platform.
A typical multi-GPU AI workstation setup requires driver tuning, container orchestration, framework integration, GPU memory planning, and runtime conflict resolution. The hard work is not installing tools one by one—it is making them coexist reliably in a private AI infrastructure you can support long term.
Engineering Challenges Solved
Day-One Experience
Delivered with working demos, not just installed tools
From power-on to real AI workflows in minutes. This platform is delivered as a ready-to-use local AI workstation environment—validated demos, not a pile of installers—so you see value on day one.
Document Q&A Demo
- •Upload a document
- •Generate embeddings automatically
- •Store vectors locally
- •Ask questions against your data
AI Automation Demo
- •Pre-loaded n8n workflow
- •Local AI agent orchestration
- •Context-aware responses
- •Business workflow ready
Local LLM Validation
- •Large local model ready to test
- •Immediate performance validation
- •No API key required
- •Day-one proof of readiness
What's Included
- Pre-installed AI stack
- Validated demo workflows
- Docker-based architecture
- JupyterLab and VS Code
- Remote access and monitoring
- Backup and storage configuration
Hardware
AI Workstation Configuration (Customizable)
Each Turnkey AI Platform is tailored to customer requirements. A typical high-performance configuration may include:
- •AMD Ryzen Threadripper PRO 9975WX
- •2 × NVIDIA RTX 6000 Blackwell (96GB each)
- •384GB DDR5 ECC memory
- •SSD for Ubuntu + AI stack
- •SSD for Windows
- •Dedicated Phison AI cache drive
- •High-capacity storage for data and backups
Example Use Cases
Flexible enough for multiple deployment models
Explore configurations for enterprise copilots, on-prem AI, and creative pipelines—then talk to our team about a build that matches your GPUs and compliance needs.
Enterprise AI Copilot
Deploy a secure internal assistant for policies, documentation, and knowledge search.
AI Automation Platform
Create intelligent workflows that combine local LLMs with business logic and events.
Creative AI Workstation
Support visual, video, and voice generation in a fully local creative environment.
AI Development Sandbox
Prototype, benchmark, and iterate on local AI applications without cloud dependency.
Optional Engineering Services
Designed for complex deployments
Advanced AI environments may require additional engineering and validation, including custom stack integration, workflow optimization, performance tuning, and ongoing support.
Where to Buy
ABS AI Workstations are available through Newegg. Start with a base system and upgrade to a fully engineered Turnkey AI Platform.
View Available SystemsDeployment
Local AI vs Cloud AI
Running inference and training in the public cloud can mean recurring API fees, variable latency, and data leaving your environment. A turnkey on-prem AI platform keeps sensitive workloads on a private AI infrastructure you control—ideal when policy requires on-prem AI or air-gapped operation.
| Topic | Typical cloud AI | Turnkey AI Platform |
|---|---|---|
| Cost model | Ongoing API and compute charges | Hardware + platform investment you amortize |
| Data residency | Data may transit or reside outside your network | Stays on your workstation / LAN |
| Connectivity | Requires stable internet for many flows | Core stacks run offline-capable |
| Throughput limits | Provider rate limits and quotas | Local GPUs under your control |
| Latency | Network round-trips to APIs | Local inference and batch jobs on-box |
Ideal for teams comparing local AI vs cloud for governance, cost predictability, and repeatable AI deployment on a single platform.
The Turnkey AI Platform is built for organizations that want to deploy local AI infrastructure, run LLMs on-premise, and ship RAG and automation workflows—without cloud dependency for core paths or weeks of integration work.
Frequently Asked Questions
- What is a turnkey AI platform?
- A turnkey AI platform is a pre-integrated environment—hardware plus validated software stacks and demos—so teams can run local AI workloads without assembling drivers, containers, and frameworks from scratch.
- Can I run LLMs locally on this workstation?
- Yes. The stack is built for local LLM inference and chat using tools such as Ollama, vLLM, and Open WebUI. Maximum practical model size depends on GPU memory, quantization, and configuration—larger models typically need more VRAM or multi-GPU setups.
- What is RAG and how is it supported?
- RAG (Retrieval-Augmented Generation) lets models answer using your documents. The platform supports local embeddings, vector storage, and workflow tools so you can build private RAG pipelines without sending data to the cloud.
- Do I need cloud APIs?
- No. Core workflows are designed to run entirely on the workstation. You may optionally connect external services, but local operation is a first-class path.
Ready to Deploy?
Skip the setup. Start building immediately.
Get a recommended configuration, deploy a fully engineered AI environment, and bring local AI capabilities in-house.