TurboQuant's performance and quality across different GPU backends (CUDA vs. Metal).

## Context

@spiritbuun's CUDA fork is now the performance leader:
- **PPL: -1.17% vs q8_0** (beats baseline quality)
- **Prefill: 99.6%** of q8_0
- **Decode: 97.5%** of q8_0
- **128K context** on RTX 3090 24GB, Q6 Qwen3.5 27B

Repo: github.com/spiritbuun/llama-...

Our Metal implementation: 99% prefill, +1.1% PPL, but only 88-90% decode.

## Task

Go through buun's latest commits and identify optimizations we can port to Metal. Cherry-pick what's portable, document what's CUDA-only.

### Already ported
- [x] Norm correction (PPL +1.6% → +1.1%) — merged to main
- [x] Register centroid LUT — tested, spills on Metal (CUDA-only)

### To review
- [ ] Latest decode dequant optimizations (fattn-common.cuh)
- [ ] V dequant path (separate from K dot-product path)
- [ ] Batched uint8 loads for qs/signs (3 loads per 8 elements vs 16)
- [ ] turbo4 V_DOT2 half2 path — any Metal equivalent?
- [ ] AMD RDNA v_dot2_f32_f16 path — relevant for our AMD testers
- [ ] Any new norm correction refinements since our port
- [ ] FWHT rotation implementation differences
- [ ] Prefill dequant-then-attend (we're blocked on turbo3→f16 cast)

### Files to review
```
ggml/src/ggml-cuda/fattn-common.cuh # FA dequant (decode hot path)
ggml/src/ggml-cuda/turbo-quant-cuda.cuh # Quantize + norm correction
ggml/src/ggml-cuda/turbo-wht.cu # FWHT rotation
ggml/src/ggml-cuda/fattn-vec.cuh # Vec attention path
```

### Attribution
All ported optimizations must credit @spiri...