Table of Contents
AI Workload Types and Their Hardware Requirements
AI workloads fall into two categories with distinct hardware requirements:
Training
Training neural networks requires maximum GPU VRAM (larger models need more), high CPU performance for data preprocessing and augmentation, fast NVMe storage for dataset loading, and large system RAM for holding dataset batches. Training typically runs for hours or days.
Inference
Serving trained models for real-time predictions prioritizes GPU throughput, low latency, and reliable availability. Many inference workloads can run on smaller GPUs if the model fits in VRAM.
VRAM Requirements by Model Size
| Model | Min VRAM | Optimal |
|---|---|---|
| Stable Diffusion 1.5 | 4 GB | 8 GB |
| Stable Diffusion XL | 8 GB | 12 GB |
| LLaMA 7B (FP16) | 14 GB | 24 GB |
| LLaMA 13B (FP16) | 26 GB | 48 GB |
Power Down GPU Infrastructure
Our RTX 4090 server provides 24 GB GDDR6X VRAM with 1,008 GB/s memory bandwidth — sufficient for most inference workloads including SDXL, LLaMA 7B-13B (quantized), and other popular open-source models.
FAQ
Can I fine-tune an LLM on your GPU server?
Yes. With 24 GB VRAM, you can fine-tune smaller models (7B parameters) using QLoRA techniques that reduce VRAM requirements dramatically. Contact us for availability and scheduling.
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GPU Server for AI and ML Workloads
Threadripper Pro + RTX 4090 GPU server at Power Down — 24GB VRAM for AI inference.
