Creator AI Rig
Balanced single-GPU workstation for content generation, local assistants, and accelerated creative workflows.
Why this build: Designed for high-VRAM creator workflows where local iteration is the priority rather than rack-scale deployment.
Best for:- Stable Diffusion users and AI artists
- Solo creators building local copilots
- Developers prototyping 7B–13B local LLM apps
Performance:- Single-GPU planning baseline for image generation, video enhancement, and local-assistant workflows
- Planning fit for local 7B–13B-class inference and RAG development; actual responsiveness depends on model, quantization, and software stack
- Combines creator workloads, upscaling, and local AI tooling on one workstation-class path
Upgrade path: Move to a dual-GPU motherboard platform or increase NVMe capacity for larger datasets and checkpoint libraries.
GPU Configuration: 1 × RTX 4090
CPU: 1 × Ryzen 9 9950X
Use Case: Image/video generation, RAG apps, and daily local inference development.
Load & Customize →LoRA Fine-Tuning Workstation
High-VRAM dual-GPU planning baseline for parameter-efficient fine-tuning and medium-scale training workflows.
Why this build: Designed to balance workstation ergonomics, GPU memory, and CPU resources for repeated LoRA and QLoRA fine-tuning cycles.
Best for:- ML engineers running LoRA and QLoRA experiments
- Teams validating model adaptation before cloud scale-out
- Practitioners processing medium-sized private datasets locally
Performance:- Dual-GPU layout provides two accelerators for parallel experiment scheduling
- Workstation CPU resources support preprocessing and tokenization alongside GPU jobs
- Planning baseline for LoRA/QLoRA tuning and evaluation; actual throughput depends on model, precision, batch size, and software stack
Upgrade path: Scale to four GPUs on the same platform and expand system RAM for larger batch sizes and concurrent jobs.
GPU Configuration: 2 × RTX 6000 Ada
CPU: 1 × Threadripper PRO 7975WX
Use Case: LoRA/QLoRA fine-tuning, quantization experiments, and heavier data preprocessing.
Load & Customize →Enterprise Training Node
Datacenter-oriented node profile for organizations planning production-scale AI training and inference capacity.
Why this build: Targets enterprise teams that need datacenter-aligned hardware behavior to de-risk production training and serving architecture decisions.
Best for:- Platform teams building internal AI infrastructure
- Organizations piloting production-scale model training
- Inference and capacity-planning exercises
Performance:- Datacenter-oriented GPU configuration for training and inference capacity planning
- Large-memory GPU and CPU platform intended for large-batch planning; measured performance depends on framework and workload
- Reference architecture for production-like load-test design, not an SLA prediction
Upgrade path: Evolve into a multi-node fabric with shared storage and orchestration for full-scale distributed training deployments.
GPU Configuration: 4 × RTX PRO 6000 Blackwell Server Edition
CPU: 2 × EPYC 9654
Use Case: Enterprise fine-tuning, distributed inference, evaluation, and capacity planning.
Reference architecture — Builder handoff intentionally unavailable.- 4-GPU topology is outside the current 1-2 GPU Builder-validated scope.
- 5050W planning target exceeds the current 2000W single-PSU Builder model.
