Hardware Requirements for Local AI
Which GPU, how much RAM, and storage do you need for different AI models? A comprehensive hardware guide.
Table of Contents
01GPU – The Heart of Local AI
The graphics card (GPU) is the most important component for local AI. It handles the massively parallel computations needed for neural networks. The decisive factor is VRAM (Video RAM) – the GPU's memory. The more VRAM, the larger the models you can load and the higher resolutions are possible.
02VRAM Requirements by Model Type
Different models have different VRAM requirements:
- SD 1.5 (Image Generation): 4–6 GB VRAM – runs on almost any modern GPU
- SDXL (Image Generation): 8–12 GB VRAM – Recommended: RTX 3060 12GB or better
- Flux.1 (Image Generation): 12–24 GB VRAM – Recommended: RTX 4070 Ti Super or better
- Wan 2.2 1.3B (Video): 8–12 GB VRAM – for short clips at lower resolution
- Wan 2.2 14B (Video): 24+ GB VRAM – Recommended: RTX 4090 or RTX 5090
- LLMs 7B (Language): 6–8 GB VRAM – with quantization (GGUF Q4) possible on 4GB
- LLMs 70B (Language): 40+ GB VRAM – requires Multi-GPU or CPU offloading
03Recommended GPUs
An overview of recommended graphics cards for local AI:
- Entry-level: NVIDIA RTX 3060 12GB (~$300) – Good price-performance ratio, sufficient for SDXL and small LLMs
- Mid-range: NVIDIA RTX 4070 Ti Super 16GB (~$800) – Ideal for most workflows, good Flux performance
- High-end: NVIDIA RTX 4090 24GB (~$1,800) – Top performance, Wan 2.2 14B at acceptable speed
- Enthusiast: NVIDIA RTX 5090 32GB (~$2,200) – Maximum performance and VRAM for all current models
- AMD Alternative: RX 7900 XTX 24GB (~$900) – Good VRAM, but limited software compatibility
04System RAM
System RAM is especially important when loading models and for CPU offloading. At least 16 GB RAM is recommended, 32 GB is ideal. For very large models (70B LLMs) or when you frequently switch between models, 64 GB makes sense. RAM is relatively inexpensive and an easy upgrade.
05Storage
AI models can be very large. A single SDXL model is about 6 GB, Flux models are 12–24 GB, and LLMs can be over 40 GB. Plan for at least 500 GB of free storage on an SSD. An NVMe SSD is recommended as it significantly speeds up model loading. A dedicated drive for models is a good investment.
06NVIDIA vs. AMD vs. Intel
NVIDIA dominates the local AI space thanks to CUDA and comprehensive software support. Almost all AI tools are optimized for NVIDIA. AMD GPUs are supported via ROCm, but compatibility is more limited. Intel Arc GPUs offer basic support via IPEX but are not yet mature. For the best workflow, we currently recommend NVIDIA.
Hardware Recommendations
The best hardware for local AI generation. Our recommendations based on price-performance and compatibility.
Graphics Cards (GPU)
NVIDIA RTX 3060 12GB
EntryBest entry-level model for local AI. 12 GB VRAM is sufficient for SDXL and small LLMs.
from ~$300NVIDIA RTX 4070 Ti Super 16GB
RecommendedIdeal mid-range GPU. 16 GB VRAM for Flux, SDXL, and medium-sized LLMs.
from ~$800NVIDIA RTX 4090 24GB
High-EndHigh-end GPU for demanding models. 24 GB VRAM for Wan 2.2 14B and large LLMs.
from ~$1,800NVIDIA RTX 5090 32GB
EnthusiastMaximum performance and VRAM. 32 GB for all current and future AI models.
from ~$2,200Memory (RAM)
Storage (SSD)
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