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 →Multi-GPU Research Rig
Four-GPU research box for larger context experiments, distributed inference, and model comparison workloads.
Why this build: Built for research-heavy teams that need multiple GPUs in one node for side-by-side model testing and distributed inference patterns.
Best for:- Applied AI research groups
- Inference benchmarking and model comparison pipelines
- Teams testing long-context and multi-model orchestration
Performance:- Four-GPU topology provides four accelerators for concurrent model and evaluation scheduling
- Four 96GB GPUs provide 384GB aggregate VRAM; usable model placement depends on framework and parallelism strategy
- Planning baseline for batch inference and synthetic-data workflows; measured throughput is workload-specific
Upgrade path: Add high-speed networking and scale to a small cluster for multi-node experiments and distributed training.
GPU Configuration: 4 × RTX PRO 6000 Blackwell Workstation Edition
CPU: 1 × Threadripper PRO 7995WX
Use Case: Model evaluation pipelines, multi-GPU training prototypes, and synthetic data generation.
Reference architecture — Builder handoff intentionally unavailable.- 4-GPU topology is outside the current 1-2 GPU Builder-validated scope.
- 4150W planning target exceeds the current 2000W single-PSU Builder model.
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.
