When building an AI workstation, everyone talks about GPUs—but system RAM is just as important for smooth performance. How much do you really need? Let’s break it down.
✅ Why RAM Matters for AI
- Model Loading: Models load into system memory before being moved to the GPU.
- CPU Offload: If your GPU runs out of VRAM, parts of the model spill into system RAM.
- Multi-Tasking: Running an inference server, a web UI, and a browser at the same time requires more memory.
Recommended RAM for Different Model Sizes
| Model Size | Typical VRAM Need | Recommended System RAM |
|---|---|---|
| 7B | 6–8GB (INT4/INT8) | 16–32GB |
| 13B | 10–12GB | 32GB |
| 30B | 20GB+ (with CPU offload) | 64GB |
| 70B | Multi-GPU setups only | 128GB+ |
Quick tip: For 7B models, 16GB works, but 32GB gives breathing room.
✅ What About Other AI Workloads?
- Stable Diffusion image generation:
- 16GB RAM is fine for inference if you have a GPU with 12GB VRAM.
- Fine-tuning models:
- Small LoRA fine-tunes: 32GB recommended.
- Full model training: 64GB+ needed.
When to Upgrade
Upgrade if:
- You see frequent out-of-memory crashes when using CPU offload.
- You plan on running multiple models at once.
- You want future-proofing for larger models.
✅ Bottom Line
- 7B models? 16GB minimum, 32GB ideal.
- 13B models? Go with 32GB.
- 30B or fine-tuning? Start at 64GB.
- For massive models (70B+), you’ll need 128GB+ or multi-GPU setups.
Rule of thumb: If budget forces you to choose, invest in GPU VRAM first—it impacts performance more than system RAM.
