VRAM Optimization: Large Models on Small GPUs
Techniques and tricks to save VRAM and run larger AI models on your GPU.
Table of Contents
01Why VRAM Optimization Matters
VRAM is the biggest limitation in local AI. Modern models are getting larger, but consumer GPUs have limited memory. The good news: there are numerous techniques to drastically reduce VRAM usage, often with only minimal quality loss.
02Quantization
Quantization reduces the numerical precision of model weights:
- FP32 → FP16: Halves memory with minimal quality loss. Standard for most applications.
- FP16 → FP8: Another halving. Very useful for Flux and large models. Slight quality loss.
- FP16 → NF4/INT4: Fourfold reduction. Very common for LLMs (GGUF Q4). Noticeable but acceptable quality loss.
- GGUF Format: Flexible quantization format for LLMs. Allows various quality levels (Q2 to Q8).
03Model Offloading
With model offloading, parts of the model are moved from GPU VRAM to system RAM or even to disk. In ComfyUI, you can activate this via the command line: '--lowvram' or '--novram'. For LLMs, Ollama and llama.cpp offer automatic GPU/CPU split. The downside: generation becomes significantly slower as data is constantly copied back and forth.
04Attention Slicing & VAE Tiling
These techniques reduce peak VRAM usage during generation:
- Attention Slicing: Computes attention layers in smaller chunks instead of all at once. Saves VRAM at the cost of speed.
- VAE Tiling: Decodes the image in tiles instead of completely. Enables higher resolutions with the same VRAM.
- xformers / Flash Attention: Optimized attention implementations that both save VRAM and are faster.
- Torch Compile: Compiles the model for optimized execution. Longer startup time but faster generation.
05Practical VRAM-Saving Tips
Immediately actionable tips:
- Close other GPU programs (browser with hardware acceleration, games, etc.) before generation
- Use FP8 models when available – the quality loss is negligible for most purposes
- Reduce batch size to 1 – each additional image in the batch doubles the VRAM requirement
- Generate at lower resolution and then upscale with an upscaler
- Use model caching intelligently: don't load multiple large models simultaneously
- For video generation: start with short clips and low resolution for testing
06VRAM Monitoring
Monitor your VRAM usage with tools like nvidia-smi (command line), GPU-Z (Windows), or nvtop (Linux). In ComfyUI, the status bar shows current VRAM usage. If you regularly hit the VRAM limit, invest in a GPU with more memory – that's the most sustainable solution.
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)
* Affiliate links: If you purchase through these links, we receive a small commission at no additional cost to you. This helps us keep ComfyVault free.
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RunPod
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