Multi-GPU Setups for Local LLMs

Multi-GPU configurations are necessary when model parameters and context history exceed the VRAM limit of a single graphics card.

WHY IT MATTERS

Single consumer GPUs cap out at 24GB VRAM, restricting your ability to run 70B models at high precision. Configuring multi-GPU nodes unlocks enterprise-grade model execution.

GO DEEPER

• Tensor parallelism: Splits individual model layers across multiple GPUs to reduce latency.
• Pipeline parallelism: Distributes sequential model layers across GPUs for larger memory capacity.
• PCIe bandwidth constraints: Requires PCIe 4.0/5.0 x16 slots to prevent multi-GPU interconnect bottlenecks.
• NVLink advantages: Enables high-speed direct GPU memory transfer on supported enterprise hardware.
• 70B model split: Running 70B models requires 2x 24 GB GPUs using 4-bit quantization.
• Power supply scaling: Dual high-end GPU systems demand high-wattage power supplies (1200W+).

THE BOTTOM LINE

Add a second GPU when your model parameters and KV cache outgrow single-card 24GB VRAM limits.