cyberneticlibrary
← library

Deploy LLMs on consumer hardware

llama-cppskillsetup L3★9,423
Orchestra-Research/AI-Research-SKILLs ↗
What it does

Run LLM inference on CPU and non-NVIDIA hardware with quantization

Best for

Edge deployment and local inference on Apple Silicon and non-NVIDIA hardware without Docker complexity.

Inputs
  • · GGUF-quantized model file
  • · context window size
  • · hardware accelerator flag (Metal/ROCm/CUDA)
Outputs
  • · token stream (stdout or server)
  • · OpenAI-compatible API responses
  • · inference metrics
Requires
  • · llama.cpp binary
  • · GGUF model repository
  • · optional: Metal (macOS) / ROCm (AMD) / CUDA (NVIDIA)
Preconditions

Model must be GGUF format; CPU fallback always available; sufficient RAM for quantized model

Failure modes
  • · Quantization artifacts at very low bits (Q2_K)
  • · Slow inference on CPU without GPU offload
  • · Context length exceeded
  • · Memory overflow on embedded devices
Trust signals
  • · Pure C/C++ no dependencies
  • · Metal acceleration on M1/M2/M3
  • · OpenAI API compatibility