Open-Source Inference Reaches Production Speed: llama.cpp Tensor Parallelism Breakthrough

llama.cpp's latest build closes the performance gap with commercial inference engines like vLLM, eliminating the traditional speed-versus-quality tradeoff for open-source users. By implementing efficient tensor parallelism across multiple GPUs, the update maintains 70+ tokens/second throughput while preserving higher-quality quantization—meaning faster code generation with fewer subtle errors. This breakthrough has immediate implications for production workloads, enabling cost-effective local inference on commodity hardware without sacrificing accuracy.