The kit to develop, fine-tune, and eval models locally. A high-VRAM GPU (24 GB minimum), a strong multi-core CPU, 64 GB of RAM, fast NVMe for datasets, and a CUDA-ready build. Your data, your hardware, no per-token bill.
Plans
Choose a plan that fits your needs and budget
Item List
5Memory & Storage
2 items
Compute
2 items
Platform
1 items| Item | Category | Specs | Qty | Price | Link |
|---|---|---|---|---|---|
 | Motherboard | ChipsetX670 PSU1000 W Gold SlotsPCIe 5.0 | 1 | $280 | View Shop |
Plan Totals$1,930
Memory & Storage
2Total$1,930
FAQ
Common questions about this kit
One big GPU or two small?
For dev and research, one big-VRAM GPU — it fits a single model that two smaller ones cannot, and simplifies everything. Scale to multi-GPU only when you outgrow it.
Why 24 GB minimum?
Fine-tuning a 7B to 13B model in mixed precision fits in 24 GB; smaller and you are fighting out-of-memory more than you are training. VRAM is the budget.
CPU matters for ML?
For data loading and preprocessing, yes — a slow CPU starves the GPU. 8 cores and fast NVMe keep the GPU fed during training.
Local vs cloud for dev?
Develop and debug locally for free; run the big final training on cloud GPUs you rent by the hour. Best of both — iterate cheap, scale expensive.
User Reviews
5.0 / 5.0
AI dev workstation is my budget-LLM PC scaled up — one-big-GPU-over-two-small and 24GB-minimum are exactly right. VRAM is the budget.