PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday
  Home Clusters
Authors: name: Zonghang Li, name: Tao Li, name: Wenjiao Feng, name: Mohsen Guizani, name: Hongfang Yu

Emergency of DeepSeek R1 and QwQ 32B have broken through performance barriers
for running frontier large language models (LLMs) on home devices. While
consumer hardware is getting stronger and model quantization is improving,
existing end-side solutions still demand GPU clusters, large RAM/VRAM, and high
bandwidth, far beyond what a common home cluster can handle. This paper
introduces prima.cpp, a distributed inference system that runs 70B-scale models
on everyday home devices using a mix of CPU/GPU, low RAM/VRAM, Wi-Fi, and
cross-platform support. It uses mmap to manage model weights and introduces
piped-ring parallelism with prefetching to hide disk loading. By modeling
heterogeneity in computation, communication, disk, memory (and its management
behavior), and OS, it optimally assigns model layers to each device's CPU and
GPU, further reducing token latency. An elegant algorithm named Halda is
proposed to solve this NP-hard assignment problem. We evaluate prima.cpp on a
common four-node home cluster. It outperforms llama.cpp, exo, and dllama on
30B+ models while keeping memory pressure below 6%. This brings frontier
30B-70B models, such as Llama 3, DeepSeek R1, Qwen 2.5, and QwQ to home
assistants, making advanced AI truly accessible to individuals. The code is
open source and available at https://github.com/Lizonghang/prima.cpp.