Hardware & Performance
Choose the right hardware and optimize AI workloads for speed and cost.
The Right Hardware Makes All the Difference
AI inference speed is directly tied to your GPU's VRAM and compute power. A 7B-parameter model runs comfortably on 8 GB of VRAM, but a 70B model needs 40 GB or more without quantization. Understanding these constraints helps you pick the right card for your use case without overspending.
Wizard Tech Services builds custom PCs optimized for AI workloads, from single-GPU inference rigs to multi-GPU training setups. We also offer AI analytics services to help you extract value from your data.
VRAM Is King
For local AI, VRAM matters more than raw compute. An RTX 5060 Ti 16 GB can run most 13B models, while an RTX 5090 with 32 GB handles larger models at full speed. Quantization (GGUF, AWQ) lets you trade minor quality for major VRAM savings.
Cloud vs. Local
Cloud GPUs (AWS, Lambda, RunPod) give you on-demand access to A100s and H100s for training and heavy inference. Local hardware is better for always-on inference, privacy-sensitive workloads, and avoiding recurring API costs.