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+As a `developer`, I `want to research local LLM inference options`, so that `we can choose the best approach for running models without external servers`.
+
+# SYNOPSIS
+
+Research spike to evaluate llama.cpp, Hugging Face CLI, and other options for local inference.
+
+# DESCRIPTION
+
+Before building the local provider, we need to understand:
+
+1. **Inference engines**: Evaluate options like llama.cpp (via Ruby bindings or CLI), Hugging Face transformers, or other local inference tools
+2. **Ruby integration**: Determine if we should use Ruby bindings (e.g., `llama_cpp.rb` gem) or shell out to a CLI tool
+3. **Hugging Face integration**: Understand how to download GGUF/GGML models, whether to use `huggingface-cli` or direct API calls
+4. **GPU support**: Verify CUDA and ROCm acceleration works on Linux
+5. **Model format**: Determine which quantized model formats to support (GGUF recommended for llama.cpp)
+
+Deliverable: A written recommendation document with:
+- Recommended approach
+- Required dependencies
+- Example code showing basic inference working
+- Known limitations
+
+# SEE ALSO
+
+* [ ] https://github.com/ggerganov/llama.cpp
+* [ ] https://github.com/yoshoku/llama_cpp.rb
+* [ ] https://huggingface.co/docs/huggingface_hub/guides/cli
+* [ ] lib/elelem/net/ (existing provider implementations)
+
+# Tasks
+
+* [ ] TBD (filled in design mode)
+
+# Acceptance Criteria
+
+* [ ] Document exists with clear recommendation
+* [ ] Proof-of-concept code demonstrates loading a model and generating a response
+* [ ] GPU acceleration tested on at least one platform (CUDA or ROCm)
+* [ ] Decision made: Ruby bindings vs CLI wrapper
+* [ ] Decision made: Model download strategy (HF CLI vs direct download)