llama-cpp

To run LLM inference on CPU and non-NVIDIA GPUs, use GGUF-formatted models with the lightweight C/C++ runtime. This approach supports Apple Silicon, AMD/Intel GPUs, and CPU-only hosts without requiring heavy Python stacks or CUDA-dependent infrastructure.

Yes, you can deploy chat models on Apple Silicon and edge devices like a Raspberry Pi. Using Q4_K_M GGUF quantization with optimized CPU paths or Metal builds for M1/M2/M3/M4 chips allows local assistants to run efficiently on resource-constrained hardware.

GGUF quantization uses Q2–Q8 formats to reduce memory usage and increase inference speed for 7B–70B models. It allows developers to trade off memory, speed, and quality to fit LLM inference within the constraints of local machines and edge deployments.

You can set up an OpenAI-compatible server for local LLM inference by serving a GGUF model through the llama.cpp server mode. This supports Docker deployment, monitoring, and HTTP queries, providing a local API endpoint for chat and batch processing.

Yes, hybrid offloading supports layer offloading to GPUs using CUDA, ROCm, or Metal builds. Combined with thread and batch tuning, this maximizes hardware utilization and improves inference performance for production environments.

Running LLMs without CUDA support requires GGUF-formatted models and a lightweight C/C++ runtime, limiting you to compatible quantization formats. While it enables inference on AMD/Intel GPUs and CPUs, performance depends on available memory, thread tuning, and optional Metal or ROCm acceleration.