Running large language models across multiple GPUs is not just about adding cards—it's about choosing the right parallelism strategy. This analysis breaks down two dominant approaches: layer splitting, which distributes model layers across GPUs and is largely immune to card count, and tensor parallelism, which splits individual tensors and is highly sensitive to interconnect bandwidth. For teams with 3-4 GPUs, the decision hinges on hardware topology: if you have high-speed NVLink, tensor parallelism can deliver lower latency; if you're on PCIe, layer splitting often avoids the communication bottleneck. The post also compares vLLM, llama.cpp, and TensorSharp implementations, showing that software maturity and kernel optimization often matter more than the raw number of GPUs. This is essential reading for anyone building small-scale inference infrastructure without access to massive clusters.
A practical guide to choosing between layer splitting and tensor parallelism for multi-GPU LLM inference, focusing on interconnect sensitivity and card count trade-offs.