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+# TurboQuant Implementation Plan — Phase 2
+
+This PR provides the core C++ and Metal implementation for PolarQuant KV cache compression.
+
+## Components Added
+1. **llama-turbo.h / .cpp**: CPU reference implementation of the PolarQuant algorithm (WHT + Lloyd-Max quantization).
+2. **ggml-metal-turbo.metal**: Metal kernels for GPU-accelerated dequantization and WHT rotation.
+
+## Integration Steps for llama.cpp
+To integrate this into a clean `llama.cpp` checkout:
+
+1. **Add to ggml-metal.metal**:
+   - Copy the kernels from `ggml-metal-turbo.metal` into `ggml/src/ggml-metal.metal`.
+   - Register the new kernels in `ggml-metal.m`.
+
+2. **Add to llama.cpp**:
+   - Include `llama-turbo.h` in `llama.cpp`.
+   - Add `GGML_TYPE_TURBO4` to the `ggml_type` enum in `ggml.h`.
+   - Update the KV cache allocation logic to support the new type.
+
+3. **Update Makefile/CMake**:
+   - Add `llama-turbo.cpp` to the build sources.
+
+## Ollama Integration (The Biggest Challenge)
+Ollama builds `llama.cpp` as a submodule. To use this implementation in Ollama:
+
+1. **Custom llama.cpp Submodule**:
+   - Point Ollama's `llm/llama.cpp` submodule to our fork containing these changes.
+2. **Update CGo Bindings**:
+   - If the `llama.h` API surface changed, update `llm/llama.go` to match.
+3. **Build Ollama**:
+   - Run `go generate ./...` and then `go build .` to produce the custom Ollama binary.
+
+## Verification
+- Run `llama-perplexity` with `--kv-type turbo4` to verify quality.
+- Run `llama-bench` to verify Metal shader performance.
+  
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