INVESTIGATION DOSSIER
AI PCS
VRAM is the new RAM.
Local AI workloads are defined by GPU memory first and everything else second. We investigate VRAM ceilings, power delivery and thermals before you commit to a machine that cannot load the models you need.
INVESTIGATE A AI PCWHAT WE INVESTIGATE
- GPU VRAM capacity
- Compute and tensor throughput
- System RAM for dataset staging
- Storage bandwidth for model loading
- PSU capacity for sustained 100% load
- Thermals in multi-hour training
- Multi-GPU expansion options
- Platform PCIe lane budget
COMMON MISTAKES WE CATCH
- Buying CUDA cores when the workload needs VRAM
- A 750W PSU under a 575W GPU with a 200W CPU
- Slow storage turning model loading into dead time
- A glass showcase case recirculating hot air during 8-hour training runs
RECOMMENDED CONFIGURATIONS
AI PCS — REFERENCE BUILDS
Local LLM Starter
Ryzen 7 9700X · RTX 5070 12 GB · 64 GB DDR5
1,700–2,000 credits
Serious Inference
Ryzen 9 9950X · RTX 5090 32 GB · 128 GB DDR5
4,000–4,500 credits
Dual-GPU Lab
Threadripper · 2× RTX 5090 · 256 GB ECC
9,000 credits+
PRIORITY ORDER
- 01 VRAM capacity first
- 02 PSU with serious headroom
- 03 High-airflow chassis
- 04 Fast NVMe for model storage
- 05 64–128 GB system RAM
COMPONENTS UNDER REVIEW
Every ai pc investigation inspects these components against the criteria above — then issues a verdict.
ANSWERS ON RECORD
FREQUENTLY ASKED QUESTIONS
8–12 GB covers small models and image generation; 16–24 GB handles serious local LLMs; 32 GB+ opens large models without aggressive quantization.
Most can run light inference. Sustained training or large models expose PSU, thermal and VRAM limits quickly — exactly what our AI investigation checks.
CUDA tooling still dominates AI frameworks. AMD and Intel are improving, but for least-friction local AI we currently validate NVIDIA configurations first.