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+# MLX Backend for Qwen3-TTS (Apple Silicon)
+
+> **Status: Planned** — not being implemented now. GPU acceleration work is
+> moving to CUDA (Google Colab) first. This doc is kept as a reference for when
+> MLX becomes the priority.
+
+## Goal
+
+Run the full Qwen3-TTS pipeline natively on Apple Silicon GPU/ANE using Apple's
+[MLX](https://github.com/ml-explore/mlx) framework. This replaces ONNX Runtime
+for the LM decode loop — the primary synthesis bottleneck — where ORT's CoreML EP
+cannot help (crashes on large transformer graphs).
+
+## Why not ONNX + CoreML
+
+ONNX Runtime's CoreML execution provider is designed for CNNs and simple models.
+On ORT 2.x it crashes (SIGSEGV) when compiling 1.7B-parameter transformer graphs.
+Even if compilation succeeded, CoreML requires static shapes, which conflicts with
+the growing KV cache in the autoregressive decode loop.
+
+MLX is purpose-built for Apple Silicon LLM inference:
+- Native Metal GPU + ANE dispatch
+- Lazy evaluation with graph fusion
+- Dynamic shapes with zero overhead
+- Transformer attention and KV cache primitives built-in
+
+## Architecture
+
+```
+Current (ONNX):
+  Rust tokenizer → ORT(talker_prefill) → ORT(talker_decode)×N →
+    ORT(code_predictor)×N×15 → ORT(vocoder) → AudioFrame
+
+MLX backend:
+  Rust tokenizer → MLX bridge(talker_prefill) → MLX bridge(talker_decode)×N →
+    MLX bridge(code_predictor)×N×15 → MLX bridge(vocoder) → AudioFrame
+```
+
+Everything moves to MLX. The tokenizer stays in Rust (BPE, no change).
+The `TtsBackend` trait surface is identical — callers see no difference.
+
+## Weight format
+
+MLX uses `safetensors` + `config.json` (same convention as HuggingFace
+Transformers). The ONNX `.onnx` / `.onnx.data` files are **not used**.
+
+Two options for weight distribution:
+
+| Option | Description | Pros | Cons |
+|--------|-------------|------|------|
+| **A: Same HF repo, new subdir** | Add `mlx/` alongside `int4/` and `fp32/` | One repo, one download path | Larger repo |
+| **B: Separate HF repo** | `wavekat/Qwen3-TTS-1.7B-VoiceDesign-MLX` | Clean separation, independent versioning | Two repos to maintain |
+
+Recommendation: **Option B** — MLX weights have a different update cadence and
+the repo size difference is significant (MLX FP16 ≈ 3.4 GB vs ONNX INT4 ≈ 4.5 GB).
+
+### Weight conversion
+
+Apple's `mlx-lm` Python package converts HuggingFace checkpoints to MLX format:
+
+```bash
+pip install mlx-lm
+mlx_lm.convert --hf-path Qwen/Qwen3-TTS-1.7B-VoiceDesign \
+                --mlx-path mlx-weights/ \
+                --quantize --q-bits 4
+```
+
+The vocoder weights need separate handling — it is a custom convolutional
+architecture, not a standard LLM. A bespoke export script in
+`tools/qwen3-tts-mlx/` will be needed (similar to the existing ONNX export
+tooling in `tools/qwen3-tts-onnx/`).
+
+## Rust ↔ MLX bridge
+
+MLX is a C++/Swift library. Rust cannot call it directly. Three viable
+integration strategies:
+
+### Strategy 1: `mlx-rs` crate (pure Rust bindings)
+
+The [`mlx-rs`](https://github.com/oxideai/mlx-rs) community crate provides
+Rust bindings to MLX's C++ API via `mlx-sys`.
+
+```toml
+mlx = { version = "...", optional = true }
+```
+
+```rust
+// Conceptual — actual API depends on mlx-rs maturity
+use mlx::Array;
+let logits: Array = model.talker_decode(&embeds, &past_kv)?;
+```
+
+**Pros**: stays fully in Rust, no subprocess or FFI shim
+**Cons**: `mlx-rs` is community-maintained, may lag behind MLX releases, API
+stability is not guaranteed
+
+### Strategy 2: Swift dylib with C FFI
+
+Build a `libwavekat_mlx.dylib` in Swift that wraps MLX and exposes a C API.
+Rust calls in via `extern "C"` declarations and `cc`/`build.rs` linking.
+
+```
+crates/wavekat-tts/
+  build.rs              — compile Swift, link dylib
+  swift/WavekatMLX/
+    Sources/
+      model.swift       — MLX inference logic
+      bridge.swift      — @_cdecl C exports
+```
+
+```swift
+// bridge.swift
+@_cdecl("wavekat_mlx_synthesize")
+public func synthesize(/* C-compatible params */) -> UnsafeMutablePointer<Float> { ... }
+```
+
+```rust
+// model.rs
+extern "C" {
+    fn wavekat_mlx_synthesize(...) -> *mut f32;
+}
+```
+
+**Pros**: uses Apple's first-party MLX Swift API (most stable), stays on-device
+**Cons**: build system complexity, Swift toolchain required, FFI marshalling
+
+### Strategy 3: Swift sidecar process
+
+Build a standalone Swift CLI `wavekat-tts-mlx` that loads the model once and
+serves synthesis requests over a local Unix socket. The Rust backend spawns and
+manages the process.
+
+```
+Rust TtsBackend::synthesize()
+  └─ write SynthesizeRequest to socket (msgpack)
+  └─ read AudioFrame samples from socket
+```
+
+**Pros**: cleanest separation, Swift process can be prewarmed
+**Cons**: IPC overhead (negligible for TTS), distribution requires bundling the CLI
+
+### Recommendation
+
+Start with **Strategy 1** (`mlx-rs`) for the fastest path to working code.
+If `mlx-rs` proves too unstable, fall back to **Strategy 2** (Swift dylib).
+Strategy 3 is reserved for cases where in-process linking is not viable.
+
+## Cargo feature
+
+```toml
+# Cargo.toml
+mlx-qwen3-tts = ["dep:mlx", "dep:hf-hub", "dep:tokenizers", "dep:npyz", "dep:rand"]
+```
+
+Usage:
+```toml
+wavekat-tts = { version = "...", features = ["mlx-qwen3-tts"] }
+```
+
+`mlx-qwen3-tts` and `qwen3-tts` are independent features — callers can enable
+either or both. The `TtsBackend` trait is the common interface.
+
+## Source layout
+
+```
+src/backends/
+  qwen3_tts/         — existing ONNX backend (unchanged)
+  mlx_qwen3/
+    mod.rs           — MlxQwen3Tts struct, TtsBackend impl, ModelConfig
+    model.rs         — MLX inference: prefill, decode loop, code predictor, vocoder
+    download.rs      — HF Hub download for MLX weights
+    tokenizer.rs     — re-use or thin wrapper around qwen3_tts::tokenizer
+    sampler.rs       — re-use or thin wrapper around qwen3_tts::sampler
+```
+
+`tokenizer.rs` and `sampler.rs` are identical to the ONNX backend. Consider
+lifting them to `src/backends/qwen3_shared/` to avoid duplication (out of scope
+for this branch).
+
+## Model file layout (HF repo)
+
+```
+wavekat/Qwen3-TTS-1.7B-VoiceDesign-MLX
+  config.json
+  model.safetensors          (or sharded: model-00001-of-00003.safetensors, ...)
+  model.safetensors.index.json
+  tokenizer/
+    vocab.json
+    merges.txt
+  vocoder/
+    config.json
+    model.safetensors
+```
+
+## MLX inference design
+
+The talker LM is a standard Qwen3 transformer — MLX's `nn.TransformerBlock`
+with GQA and M-RoPE. The only custom piece is the dual-stream embedding
+(text_proj + codec_embed) built in Rust before the first MLX call.
+
+```
+prefill:
+  embeddings (Rust) → mlx.eval(talker_prefill_graph) → logits, kv_cache
+
+decode loop (×N):
+  codec_embed (Rust) → mlx.eval(talker_decode_graph) → logits, kv_cache
+
+code predictor (×N×15):
+  mlx.eval(cp_graph) → group tokens
+
+vocoder (×1):
+  mlx.eval(vocoder_graph) → waveform Float32
+```
+
+KV cache is an `mlx::Array` allocated once and grown in-place — no Rust-side
+tensor management needed for the cache itself.
+
+## Implementation order
+
+1. Validate `mlx-rs` API coverage: attention, KV cache, matmul, conv1d.
+2. Set up `tools/qwen3-tts-mlx/` export script for vocoder weights.
+3. Add `mlx-qwen3-tts` feature to `Cargo.toml`.
+4. Implement `src/backends/mlx_qwen3/download.rs` (HF Hub, new repo).
+5. Implement `src/backends/mlx_qwen3/model.rs` — talker + code predictor.
+6. Implement vocoder in MLX.
+7. Wire `TtsBackend` impl in `mod.rs`.
+8. Add `test-mlx` Makefile target.
+9. Benchmark vs ONNX CPU: measure RTF on M4 Pro.
+
+## Open questions
+
+- **mlx-rs maturity**: does it expose KV cache primitives and M-RoPE? If not,
+  we need to implement them as raw MLX ops.
+- **Vocoder export**: the vocoder is not a standard architecture — need to verify
+  safetensors export produces loadable weights without graph-level ops baked in.
+- **Quantisation**: MLX 4-bit quantisation (Q4) uses a different scheme than
+  ORT INT4. Validate audio quality parity before publishing MLX weights.
+- **macOS version floor**: MLX requires macOS 13.5+. Document this requirement.