Single GPU vs Multi-GPU: What You Need to Know

Running multiple GPUs can dramatically increase your VRAM, but it comes with complexity and cost tradeoffs.

Why Multi-GPU?

If you want to run a 70B model at FP16, you'd need 140GB of VRAM — that's 6x RTX 3090s (not practical). Multi-GPU setups let you:

- Fit larger models

- Use higher batch sizes

- Enable tensor parallelism

The Costs

- Hardware: a second high-end GPU is a significant purchase — check current retail or used prices

- Power: Two GPUs = 650W+ idle, 1000W+ under load

- Motherboard/CPU: Need PCIe slots + sufficient CPU lanes

- NVLink: Adds a hardware cost for faster GPU-to-GPU communication

Cloud vs Own Hardware

For occasional use, renting cloud GPU capacity avoids a second purchase. Pricing and availability change; check the provider for current details.

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Practical Recommendation

- 7B-13B models: Single GPU, no question

- 33B models: Single 24GB GPU with Q4 quantization — or dual 12GB

- 70B+ models: Cloud GPU or multi-GPU setup

Use the VRAM Calculator to plan your setup.