diff --git a/models/llm/qwen3-0.6b/coreml/.gitignore b/models/llm/qwen3-0.6b/coreml/.gitignore
new file mode 100644
index 0000000..234ed67
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/.gitignore
@@ -0,0 +1,4 @@
+build/
+.venv/
+*.log
+__pycache__/
diff --git a/models/llm/qwen3-0.6b/coreml/README.md b/models/llm/qwen3-0.6b/coreml/README.md
new file mode 100644
index 0000000..cee0575
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/README.md
@@ -0,0 +1,77 @@
+# Qwen3-0.6B → CoreML (LLM-on-ANE thesis test)
+
+First `llm`-class conversion in mobius. Purpose: test whether a small LLM can run
+real-time on-device and where it lands across compute units, per the research lead in
+`knowledge/coreml/ane-cpu-scheduled-matmul.md` (prefill is compute-bound → ANE candidate;
+decode is bandwidth-bound + mutates the KV cache in-graph → expected CPU/GPU per the
+Surgical Inference §6.3 state-mutation cliff).
+
+Model: [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) — 28 layers, hidden
+1024, 16 Q / 8 KV heads (GQA), head_dim 128, intermediate 3072, vocab 151,936, RoPE
+θ=1e6, per-head q/k RMSNorm, tied embeddings. Arch constants are identical to the
+in-repo `stt/qwen3-asr-0.6b` decoder, which this conversion is adapted from.
+
+## Pipeline
+
+Host owns embeddings + RoPE (outside the graph, matching the runtime split); the CoreML
+model owns the 28 transformer layers + final RMSNorm + `lm_head`, with a stateful fp16 KV
+cache. Output is `logits [1, 1, VOCAB]` for the last position only (prefill projects one
+row). See `knowledge/coreml/ane-cpu-scheduled-matmul.md` for why prefill vs decode
+placement is the interesting question.
+
+## Run
+
+```bash
+uv sync
+# Convert (downloads weights, traces, converts; ~minutes):
+uv run python convert-coreml.py --output-dir ./build --max-seq-len 512
+
+# Decode throughput + RTFx on a given engine:
+uv run python benchmark.py --model-dir ./build --compute-units CPU_AND_NE
+uv run python benchmark.py --model-dir ./build --compute-units CPU_AND_GPU
+
+# Device placement / per-op fallback ablation:
+cd ../../../../tools/coreml-cli
+uv run coreml-cli <path>/build/qwen3_0_6b_decoder_stateful.mlpackage
+uv run coreml-cli <path>/build/qwen3_0_6b_decoder_stateful.mlpackage --fallback
+```
+
+## RTFx
+
+`benchmark.py` reports **RTFx = decode tok/s ÷ 15 tok/s**, where 15 tok/s ≈ brisk TTS
+narration (a real-time downstream drain). RTFx > 1 means generation outpaces consumption.
+Prefill latency and tok/s are reported separately.
+
+## Findings (M5 Pro, 24 GB, macOS 26 / coremltools 9.0, 2026-07-16)
+
+The two graphs split exactly as the Surgical Inference §6.3 thesis predicts — **the ANE
+cliff is in-graph state mutation, not the transformer math.**
+
+**Decode graph (stateful, in-graph KV-cache mutation) — `convert-coreml.py`:**
+- `CPU_AND_NE`: **ANE REJECTED** — `ANECCompile() FAILED, error -14`. Matches §6.3: the
+  in-graph cache write is the admission cliff.
+- `CPU_AND_GPU`: **27 tok/s**, decode p50 29.6 ms/token, prefill 42.5 ms (8 tok).
+  **RTFx = 1.80× (> 1, PASS** vs 15 tok/s real-time drain). Output is coherent English, so
+  the port is numerically correct. (Python-`predict` overhead inflates per-token time; a
+  Swift runtime would be faster.)
+
+**Prefill graph (stateless, fixed seq_len=128) — `convert-prefill.py`:**
+- `CPU_AND_NE`: **ANE ACCEPTED** — compiles and runs. `MLComputePlan`: **1918 ops on the
+  Neural Engine, 1 on CPU** (rest are attribution-free consts) → essentially fully
+  ANE-resident. p50 **12.5 ms / 128 tokens**.
+- `CPU_ONLY`: 46.0 ms → the ANE is ~4× over CPU (proves it isn't silently CPU-fallback).
+- `CPU_AND_GPU`: 9.3 ms → on this M5 Pro the (very strong) GPU is marginally ahead of the
+  ANE via *standard* Core ML. The reviewer's "ANE beats the GPU on prefill" claim rests on
+  the private CPU-scheduled matmul path (see `knowledge/coreml/ane-cpu-scheduled-matmul.md`),
+  which is **not** what this standard-Core ML export uses — untested here.
+
+**Bottom line:** an LLM *can* run on the ANE — for **prefill** (compute-bound, cache-free,
+Dense-Static). Decode's KV-cache mutation is ANE-rejected and belongs on GPU/CPU, where it
+is comfortably real-time (RTFx 1.80×). This is the split-phase placement the knowledge note
+predicts.
+
+Open next steps: stateless host-owned-cache decode (caches as I/O, per §6.3) to test if
+*decode* can also reach the ANE; int8 weight streaming for the bandwidth wall; the private
+matmul path for the ANE-beats-GPU claim.
+
+> Not HF-uploaded — research/benchmark artifact, not a shipped model.
diff --git a/models/llm/qwen3-0.6b/coreml/benchmark.py b/models/llm/qwen3-0.6b/coreml/benchmark.py
new file mode 100644
index 0000000..270d90a
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/benchmark.py
@@ -0,0 +1,142 @@
+"""On-device decode benchmark for the Qwen3-0.6B CoreML decoder.
+
+Measures prefill latency and steady-state decode tokens/s, and derives an RTFx-style
+real-time factor: decode throughput divided by a real-time consumption rate.
+
+RTFx here = (decode tokens/s) / RT_TOKENS_PER_SEC, where RT_TOKENS_PER_SEC is the rate a
+downstream consumer drains tokens. Default 15 tok/s ≈ brisk TTS narration (~3-4 tokens per
+spoken word at ~4 words/s). RTFx > 1 means generation outpaces consumption — real-time.
+
+Host owns embeddings + RoPE (they live outside the decoder graph, matching the runtime
+split); the CoreML model owns the 28 transformer layers + lm_head with a stateful KV cache.
+
+Usage:
+    uv run benchmark.py --model-dir ./build --compute-units CPU_AND_NE
+    uv run benchmark.py --model-dir ./build --compute-units CPU_AND_GPU --gen-tokens 128
+"""
+
+import argparse
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+
+HEAD_DIM = 128
+ROPE_THETA = 1_000_000.0
+RT_TOKENS_PER_SEC = 15.0  # real-time consumption bar (see module docstring)
+
+
+def rope_tables(positions: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """cos/sin for given absolute positions, concatenated-halves layout. [N, HEAD_DIM]."""
+    inv_freq = 1.0 / (ROPE_THETA ** (np.arange(0, HEAD_DIM, 2, dtype=np.float64) / HEAD_DIM))
+    freqs = np.outer(positions.astype(np.float64), inv_freq)  # [N, HEAD_DIM/2]
+    emb = np.concatenate([freqs, freqs], axis=-1)  # [N, HEAD_DIM]
+    return np.cos(emb).astype(np.float32), np.sin(emb).astype(np.float32)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-dir", default="./build")
+    parser.add_argument("--model-id", default="Qwen/Qwen3-0.6B")
+    parser.add_argument("--compute-units", default="CPU_AND_NE",
+                        choices=["ALL", "CPU_AND_NE", "CPU_AND_GPU", "CPU_ONLY"])
+    parser.add_argument("--prompt", default="The key idea behind on-device inference is")
+    parser.add_argument("--gen-tokens", type=int, default=64)
+    parser.add_argument("--warmup", type=int, default=8)
+    args = parser.parse_args()
+
+    import coremltools as ct
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    mlpkg = Path(args.model_dir) / "qwen3_0_6b_decoder_stateful.mlpackage"
+    print(f"Loading {mlpkg} on {args.compute_units} ...")
+    cu = getattr(ct.ComputeUnit, args.compute_units)
+    t0 = time.time()
+    mlmodel = ct.models.MLModel(str(mlpkg), compute_units=cu)
+    print(f"  loaded/compiled in {time.time() - t0:.1f}s")
+
+    tok = AutoTokenizer.from_pretrained(args.model_id)
+    hf = AutoModelForCausalLM.from_pretrained(args.model_id, torch_dtype=torch.float32)
+    embed = hf.model.embed_tokens.weight.detach().numpy()  # [vocab, hidden]
+
+    ids = tok(args.prompt, return_tensors="np")["input_ids"][0].astype(np.int64)
+    S = len(ids)
+    print(f"Prompt tokens: {S}")
+
+    def embed_ids(token_ids: np.ndarray) -> np.ndarray:
+        return embed[token_ids][None, :, :].astype(np.float32)  # [1, N, hidden]
+
+    NEG = -1e4
+
+    def run_prefill(state):
+        h = embed_ids(ids)  # [1, S, hidden]
+        cos, sin = rope_tables(np.arange(S))
+        mask = np.triu(np.full((1, 1, S, S), NEG, dtype=np.float32), k=1)
+        out = mlmodel.predict({
+            "hidden_states": h,
+            "position_cos": cos[None],
+            "position_sin": sin[None],
+            "attention_mask": mask,
+        }, state=state)
+        return out["logits"][0, -1]  # [vocab]
+
+    def run_decode_step(state, token_id: int, pos: int):
+        h = embed_ids(np.array([token_id]))  # [1,1,hidden]
+        cos, sin = rope_tables(np.array([pos]))
+        mask = np.zeros((1, 1, 1, pos + 1), dtype=np.float32)  # attend all past + self
+        out = mlmodel.predict({
+            "hidden_states": h,
+            "position_cos": cos[None],
+            "position_sin": sin[None],
+            "attention_mask": mask,
+        }, state=state)
+        return out["logits"][0, -1]
+
+    # ---- Warmup (compile caches, ANE spin-up) ----
+    for _ in range(2):
+        st = mlmodel.make_state()
+        logits = run_prefill(st)
+        nxt = int(logits.argmax())
+        pos = S
+        for _ in range(args.warmup):
+            logits = run_decode_step(st, nxt, pos)
+            nxt = int(logits.argmax())
+            pos += 1
+
+    # ---- Timed: prefill ----
+    st = mlmodel.make_state()
+    t0 = time.time()
+    logits = run_prefill(st)
+    prefill_ms = (time.time() - t0) * 1000.0
+    nxt = int(logits.argmax())
+    pos = S
+
+    # ---- Timed: decode ----
+    decode_times = []
+    generated = [nxt]
+    for _ in range(args.gen_tokens):
+        t0 = time.time()
+        logits = run_decode_step(st, nxt, pos)
+        decode_times.append(time.time() - t0)
+        nxt = int(logits.argmax())
+        generated.append(nxt)
+        pos += 1
+
+    dt = np.array(decode_times)
+    tok_per_s = 1.0 / dt.mean()
+    p50 = np.percentile(dt, 50) * 1000
+    p99 = np.percentile(dt, 99) * 1000
+    rtfx = tok_per_s / RT_TOKENS_PER_SEC
+
+    print("\n=== Results ===")
+    print(f"compute_units       : {args.compute_units}")
+    print(f"prefill ({S} tok)    : {prefill_ms:.1f} ms  ({S / (prefill_ms/1000):.0f} tok/s)")
+    print(f"decode p50 / p99    : {p50:.2f} / {p99:.2f} ms per token")
+    print(f"decode throughput   : {tok_per_s:.1f} tok/s")
+    print(f"RTFx (vs {RT_TOKENS_PER_SEC:.0f} tok/s) : {rtfx:.2f}x  {'PASS >1' if rtfx > 1 else 'FAIL <1'}")
+    print(f"\nsample: {tok.decode(generated[:24])!r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/llm/qwen3-0.6b/coreml/convert-coreml.py b/models/llm/qwen3-0.6b/coreml/convert-coreml.py
new file mode 100644
index 0000000..80c5050
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/convert-coreml.py
@@ -0,0 +1,233 @@
+"""Convert standalone Qwen3-0.6B to a fused stateful CoreML decoder (lmHead baked in).
+
+Adapted from models/stt/qwen3-asr-0.6b/coreml/convert_decoder_fused.py — the same
+proven Qwen3 decoder graph, but pointed at the standalone LLM (Qwen/Qwen3-0.6B) loaded
+via AutoModelForCausalLM instead of the ASR "thinker.*" weight nesting.
+
+Purpose: test the "LLM-on-ANE" thesis (see knowledge/coreml/ane-cpu-scheduled-matmul.md).
+The output is a stateful decode graph (KV cache as MLState) producing logits [1, 1, VOCAB]
+for the last position — this is the *decode* path, whose in-graph cache mutation is exactly
+what the paper predicts the ANE rejects, so it is expected to land on CPU/GPU. Prefill is
+naturally stateless; profile placement with `coreml-cli` after conversion.
+
+Usage:
+    uv run convert-coreml.py --output-dir ./build
+    uv run convert-coreml.py --model-id Qwen/Qwen3-0.6B --max-seq-len 512
+"""
+
+import argparse
+import math
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# Qwen3-0.6B architecture constants (identical to Qwen3-ASR-0.6B's text decoder)
+NUM_LAYERS = 28
+NUM_Q_HEADS = 16
+NUM_KV_HEADS = 8
+HEAD_DIM = 128
+HIDDEN_SIZE = 1024
+INTERMEDIATE_SIZE = 3072
+VOCAB_SIZE = 151_936
+GQA_REPEAT = NUM_Q_HEADS // NUM_KV_HEADS  # 2
+
+
+def rotate_half(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    if n_rep == 1:
+        return hidden_states
+    batch, num_kv_heads, slen, head_dim = hidden_states.shape
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_kv_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_kv_heads * n_rep, slen, head_dim)
+
+
+class FusedStatefulQwen3Decoder(nn.Module):
+    """28 Qwen3 decoder layers + final RMSNorm + lm_head, with stateful KV cache.
+
+    Outputs logits for the last query position only (prefill projects one row).
+    """
+
+    def __init__(self, layers, final_norm, lm_head, max_seq_len: int = 512):
+        super().__init__()
+        self.layers = layers
+        self.final_norm = final_norm
+        self.lm_head = lm_head
+        self.max_seq_len = max_seq_len
+        self.scale = 1.0 / math.sqrt(HEAD_DIM)
+
+        for i in range(NUM_LAYERS):
+            self.register_buffer(
+                f"k_cache_{i}",
+                torch.zeros(1, NUM_KV_HEADS, max_seq_len, HEAD_DIM, dtype=torch.float16),
+            )
+            self.register_buffer(
+                f"v_cache_{i}",
+                torch.zeros(1, NUM_KV_HEADS, max_seq_len, HEAD_DIM, dtype=torch.float16),
+            )
+
+    def forward(self, hidden_states, position_cos, position_sin, attention_mask):
+        q_len = hidden_states.shape[1]
+        end_step = attention_mask.shape[-1]
+        past_kv_len = end_step - q_len
+
+        cos = position_cos.unsqueeze(1)
+        sin = position_sin.unsqueeze(1)
+
+        for i in range(NUM_LAYERS):
+            layer = self.layers[i]
+            k_cache = getattr(self, f"k_cache_{i}")
+            v_cache = getattr(self, f"v_cache_{i}")
+
+            residual = hidden_states
+            hidden_states = layer.input_layernorm(hidden_states)
+
+            attn = layer.self_attn
+            q = attn.q_proj(hidden_states)
+            k = attn.k_proj(hidden_states)
+            v = attn.v_proj(hidden_states)
+
+            q = q.view(1, q_len, NUM_Q_HEADS, HEAD_DIM).transpose(1, 2)
+            k = k.view(1, q_len, NUM_KV_HEADS, HEAD_DIM).transpose(1, 2)
+            v = v.view(1, q_len, NUM_KV_HEADS, HEAD_DIM).transpose(1, 2)
+
+            # Qwen3 has per-head q/k RMSNorm (Qwen2 does not)
+            if hasattr(attn, "q_norm"):
+                q = attn.q_norm(q)
+                k = attn.k_norm(k)
+
+            q = (q * cos) + (rotate_half(q) * sin)
+            k = (k * cos) + (rotate_half(k) * sin)
+
+            k_cache[:, :, past_kv_len:end_step, :] = k.half()
+            v_cache[:, :, past_kv_len:end_step, :] = v.half()
+
+            k_full = k_cache[:, :, :end_step, :].float()
+            v_full = v_cache[:, :, :end_step, :].float()
+
+            k_full = repeat_kv(k_full, GQA_REPEAT)
+            v_full = repeat_kv(v_full, GQA_REPEAT)
+
+            attn_weights = torch.matmul(q, k_full.transpose(2, 3)) * self.scale
+            attn_weights = attn_weights + attention_mask
+            attn_weights = F.softmax(attn_weights, dim=-1)
+            attn_output = torch.matmul(attn_weights, v_full)
+
+            attn_output = attn_output.transpose(1, 2).contiguous()
+            attn_output = attn_output.view(1, q_len, NUM_Q_HEADS * HEAD_DIM)
+            hidden_states = attn.o_proj(attn_output)
+            hidden_states = residual + hidden_states
+
+            residual = hidden_states
+            hidden_states = layer.post_attention_layernorm(hidden_states)
+            mlp = layer.mlp
+            gate = mlp.gate_proj(hidden_states)
+            up = mlp.up_proj(hidden_states)
+            hidden_states = mlp.down_proj(F.silu(gate) * up)
+            hidden_states = residual + hidden_states
+
+        last_hidden = hidden_states[:, -1:, :]
+        last_hidden = self.final_norm(last_hidden)
+        logits = self.lm_head(last_hidden)
+        return logits
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert standalone Qwen3-0.6B to stateful CoreML decoder")
+    parser.add_argument("--model-id", default="Qwen/Qwen3-0.6B")
+    parser.add_argument("--max-seq-len", type=int, default=512)
+    parser.add_argument("--output-dir", default="./build")
+    parser.add_argument("--compute-units", default="ALL",
+                        choices=["ALL", "CPU_AND_NE", "CPU_AND_GPU", "CPU_ONLY"])
+    args = parser.parse_args()
+
+    MAX_SEQ_LEN = args.max_seq_len
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    from transformers import AutoModelForCausalLM
+
+    print(f"Loading {args.model_id} ...")
+    t0 = time.time()
+    model = AutoModelForCausalLM.from_pretrained(args.model_id, torch_dtype=torch.float32)
+    model.eval()
+    print(f"  loaded in {time.time() - t0:.1f}s")
+
+    base = model.model
+    layers = base.layers
+    final_norm = base.norm
+    lm_head = model.lm_head  # tied to embed_tokens for Qwen3-0.6B
+
+    # Sanity-check architecture matches our constants
+    a0 = layers[0].self_attn
+    assert len(layers) == NUM_LAYERS, f"{len(layers)} != {NUM_LAYERS}"
+    assert a0.q_proj.out_features == NUM_Q_HEADS * HEAD_DIM
+    assert a0.k_proj.out_features == NUM_KV_HEADS * HEAD_DIM
+    assert lm_head.weight.shape == (VOCAB_SIZE, HIDDEN_SIZE)
+    print(f"  layers={len(layers)} q_norm={hasattr(a0, 'q_norm')} "
+          f"q_proj={a0.q_proj.in_features}->{a0.q_proj.out_features}")
+
+    decoder = FusedStatefulQwen3Decoder(layers, final_norm, lm_head, max_seq_len=MAX_SEQ_LEN)
+    decoder.eval()
+
+    # Trace in decode shape (Q=1)
+    hidden = torch.randn(1, 1, HIDDEN_SIZE)
+    cos_in = torch.randn(1, 1, HEAD_DIM)
+    sin_in = torch.randn(1, 1, HEAD_DIM)
+    mask = torch.zeros(1, 1, 1, 5)
+    print("Tracing ...")
+    with torch.no_grad():
+        traced = torch.jit.trace(decoder, (hidden, cos_in, sin_in, mask))
+    traced.eval()
+
+    import coremltools as ct
+    print(f"coremltools {ct.__version__} — converting ...")
+
+    query_length = ct.RangeDim(lower_bound=1, upper_bound=MAX_SEQ_LEN, default=1)
+    end_step_dim = ct.RangeDim(lower_bound=1, upper_bound=MAX_SEQ_LEN, default=1)
+    inputs = [
+        ct.TensorType("hidden_states", shape=(1, query_length, HIDDEN_SIZE), dtype=np.float32),
+        ct.TensorType("position_cos", shape=(1, query_length, HEAD_DIM), dtype=np.float32),
+        ct.TensorType("position_sin", shape=(1, query_length, HEAD_DIM), dtype=np.float32),
+        ct.TensorType("attention_mask", shape=(1, 1, query_length, end_step_dim), dtype=np.float32),
+    ]
+    outputs = [ct.TensorType("logits", dtype=np.float32)]
+    states = []
+    for i in range(NUM_LAYERS):
+        states.append(ct.StateType(
+            wrapped_type=ct.TensorType(shape=(1, NUM_KV_HEADS, MAX_SEQ_LEN, HEAD_DIM), dtype=np.float16),
+            name=f"k_cache_{i}"))
+        states.append(ct.StateType(
+            wrapped_type=ct.TensorType(shape=(1, NUM_KV_HEADS, MAX_SEQ_LEN, HEAD_DIM), dtype=np.float16),
+            name=f"v_cache_{i}"))
+
+    cu = getattr(ct.ComputeUnit, args.compute_units)
+    t0 = time.time()
+    mlmodel = ct.convert(
+        traced,
+        inputs=inputs,
+        outputs=outputs,
+        states=states,
+        minimum_deployment_target=ct.target.macOS15,
+        compute_precision=ct.precision.FLOAT16,
+        compute_units=cu,
+    )
+    print(f"  converted in {time.time() - t0:.1f}s")
+
+    out_path = output_dir / "qwen3_0_6b_decoder_stateful.mlpackage"
+    mlmodel.save(str(out_path))
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/llm/qwen3-0.6b/coreml/convert-prefill.py b/models/llm/qwen3-0.6b/coreml/convert-prefill.py
new file mode 100644
index 0000000..ab6c0ae
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/convert-prefill.py
@@ -0,0 +1,159 @@
+"""Convert Qwen3-0.6B as a STATELESS prefill graph — the positive LLM-on-ANE test.
+
+The decode graph (convert-coreml.py) mutates a KV cache in-graph and is ANE-rejected
+(ANECCompile -14), matching Surgical Inference §6.3. Prefill has no cache to persist: it
+computes K/V for the whole prompt, uses them immediately, and discards them. That makes it
+a stateless Dense-Static graph — the case the paper predicts the ANE *accepts*.
+
+Fixed sequence length (static shape, no RangeDim) so the ANE compiler has everything at
+compile time. Input is embeddings + RoPE tables + causal mask (host-computed, same split as
+the runtime); output is last-position logits [1, 1, VOCAB].
+
+Usage:
+    uv run convert-prefill.py --seq-len 128 --output-dir ./build
+"""
+
+import argparse
+import math
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+NUM_LAYERS = 28
+NUM_Q_HEADS = 16
+NUM_KV_HEADS = 8
+HEAD_DIM = 128
+HIDDEN_SIZE = 1024
+VOCAB_SIZE = 151_936
+GQA_REPEAT = NUM_Q_HEADS // NUM_KV_HEADS
+
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def repeat_kv(h, n_rep):
+    if n_rep == 1:
+        return h
+    b, kv, s, d = h.shape
+    return h[:, :, None, :, :].expand(b, kv, n_rep, s, d).reshape(b, kv * n_rep, s, d)
+
+
+class StatelessPrefillQwen3(nn.Module):
+    """28 Qwen3 layers + final norm + lm_head, no KV cache. Last-position logits only."""
+
+    def __init__(self, layers, final_norm, lm_head):
+        super().__init__()
+        self.layers = layers
+        self.final_norm = final_norm
+        self.lm_head = lm_head
+        self.scale = 1.0 / math.sqrt(HEAD_DIM)
+
+    def forward(self, hidden_states, position_cos, position_sin, attention_mask):
+        q_len = hidden_states.shape[1]
+        cos = position_cos.unsqueeze(1)
+        sin = position_sin.unsqueeze(1)
+
+        for i in range(NUM_LAYERS):
+            layer = self.layers[i]
+            residual = hidden_states
+            hidden_states = layer.input_layernorm(hidden_states)
+
+            attn = layer.self_attn
+            q = attn.q_proj(hidden_states).view(1, q_len, NUM_Q_HEADS, HEAD_DIM).transpose(1, 2)
+            k = attn.k_proj(hidden_states).view(1, q_len, NUM_KV_HEADS, HEAD_DIM).transpose(1, 2)
+            v = attn.v_proj(hidden_states).view(1, q_len, NUM_KV_HEADS, HEAD_DIM).transpose(1, 2)
+
+            if hasattr(attn, "q_norm"):
+                q = attn.q_norm(q)
+                k = attn.k_norm(k)
+
+            q = (q * cos) + (rotate_half(q) * sin)
+            k = (k * cos) + (rotate_half(k) * sin)
+
+            k = repeat_kv(k, GQA_REPEAT)
+            v = repeat_kv(v, GQA_REPEAT)
+
+            attn_weights = torch.matmul(q, k.transpose(2, 3)) * self.scale
+            attn_weights = attn_weights + attention_mask
+            attn_weights = F.softmax(attn_weights, dim=-1)
+            attn_output = torch.matmul(attn_weights, v)
+
+            attn_output = attn_output.transpose(1, 2).contiguous().view(1, q_len, NUM_Q_HEADS * HEAD_DIM)
+            hidden_states = attn.o_proj(attn_output)
+            hidden_states = residual + hidden_states
+
+            residual = hidden_states
+            hidden_states = layer.post_attention_layernorm(hidden_states)
+            mlp = layer.mlp
+            hidden_states = mlp.down_proj(F.silu(mlp.gate_proj(hidden_states)) * mlp.up_proj(hidden_states))
+            hidden_states = residual + hidden_states
+
+        last_hidden = self.final_norm(hidden_states[:, -1:, :])
+        return self.lm_head(last_hidden)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-id", default="Qwen/Qwen3-0.6B")
+    parser.add_argument("--seq-len", type=int, default=128)
+    parser.add_argument("--output-dir", default="./build")
+    args = parser.parse_args()
+
+    S = args.seq_len
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    from transformers import AutoModelForCausalLM
+
+    print(f"Loading {args.model_id} ...")
+    model = AutoModelForCausalLM.from_pretrained(args.model_id, dtype=torch.float32)
+    model.eval()
+    base = model.model
+    decoder = StatelessPrefillQwen3(base.layers, base.norm, model.lm_head)
+    decoder.eval()
+
+    hidden = torch.randn(1, S, HIDDEN_SIZE)
+    cos_in = torch.randn(1, S, HEAD_DIM)
+    sin_in = torch.randn(1, S, HEAD_DIM)
+    mask = torch.triu(torch.full((1, 1, S, S), -1e4), diagonal=1)
+
+    print(f"Tracing (static seq_len={S}) ...")
+    with torch.no_grad():
+        traced = torch.jit.trace(decoder, (hidden, cos_in, sin_in, mask))
+    traced.eval()
+
+    import coremltools as ct
+    print(f"coremltools {ct.__version__} — converting stateless prefill (fixed shapes) ...")
+    inputs = [
+        ct.TensorType("hidden_states", shape=(1, S, HIDDEN_SIZE), dtype=np.float32),
+        ct.TensorType("position_cos", shape=(1, S, HEAD_DIM), dtype=np.float32),
+        ct.TensorType("position_sin", shape=(1, S, HEAD_DIM), dtype=np.float32),
+        ct.TensorType("attention_mask", shape=(1, 1, S, S), dtype=np.float32),
+    ]
+    outputs = [ct.TensorType("logits", dtype=np.float32)]
+
+    t0 = time.time()
+    mlmodel = ct.convert(
+        traced,
+        inputs=inputs,
+        outputs=outputs,
+        minimum_deployment_target=ct.target.macOS15,
+        compute_precision=ct.precision.FLOAT16,
+        compute_units=ct.ComputeUnit.CPU_AND_NE,
+    )
+    print(f"  converted in {time.time() - t0:.1f}s")
+
+    out_path = output_dir / f"qwen3_0_6b_prefill_s{S}.mlpackage"
+    mlmodel.save(str(out_path))
+    print(f"Saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/llm/qwen3-0.6b/coreml/probe-placement.py b/models/llm/qwen3-0.6b/coreml/probe-placement.py
new file mode 100644
index 0000000..28f2c82
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/probe-placement.py
@@ -0,0 +1,81 @@
+"""Probe ANE admission + placement for a converted graph.
+
+Loads the package under CPU_AND_NE and runs a predict. If the ANE rejects the graph the
+load/predict raises ANECCompile -14 (as the stateful decode graph does). Success => the ANE
+accepted it. Also times CPU_AND_NE vs CPU_AND_GPU vs CPU_ONLY, and tries MLComputePlan for a
+per-device op breakdown when available.
+"""
+
+import argparse
+import time
+from pathlib import Path
+
+import numpy as np
+
+
+def dummy_inputs(spec):
+    feeds = {}
+    for inp in spec.description.input:
+        shape = tuple(int(d) for d in inp.type.multiArrayType.shape)
+        feeds[inp.name] = np.random.randn(*shape).astype(np.float32) * 0.1
+    return feeds
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--package", required=True)
+    parser.add_argument("--iters", type=int, default=30)
+    args = parser.parse_args()
+
+    import coremltools as ct
+
+    pkg = str(Path(args.package))
+    results = {}
+    for cu_name in ["CPU_AND_NE", "CPU_AND_GPU", "CPU_ONLY"]:
+        cu = getattr(ct.ComputeUnit, cu_name)
+        try:
+            t0 = time.time()
+            m = ct.models.MLModel(pkg, compute_units=cu)
+            feeds = dummy_inputs(m.get_spec())
+            _ = m.predict(feeds)  # first predict triggers ANE compile
+            load_ms = (time.time() - t0) * 1000
+            ts = []
+            for _ in range(args.iters):
+                t = time.time()
+                m.predict(feeds)
+                ts.append((time.time() - t) * 1000)
+            ts = np.array(ts)
+            results[cu_name] = (True, f"p50 {np.percentile(ts,50):.1f}ms  p99 {np.percentile(ts,99):.1f}ms  (load+compile {load_ms:.0f}ms)")
+        except Exception as e:
+            msg = str(e).replace("\n", " ")
+            marker = "ANECCompile -14 (ANE REJECTED)" if "-14" in msg or "ANECCompile" in msg else msg[:120]
+            results[cu_name] = (False, marker)
+
+    print(f"\n=== Placement probe: {Path(args.package).name} ===")
+    for cu_name, (ok, info) in results.items():
+        print(f"{cu_name:14s} : {'OK   ' if ok else 'FAIL '} {info}")
+
+    # Per-op device breakdown (best effort; API varies by coremltools version)
+    try:
+        from coremltools.models.compute_plan import MLComputePlan
+        # MLComputePlan needs a compiled .mlmodelc; keep the MLModel alive so the temp
+        # compiled dir is not GC-deleted before the plan loads.
+        _keep = ct.models.MLModel(pkg)
+        mlmodelc = _keep.get_compiled_model_path()
+        plan = MLComputePlan.load_from_path(mlmodelc, compute_units=ct.ComputeUnit.CPU_AND_NE)
+        prog = plan.model_structure.program
+        counts = {}
+        for func in prog.functions.values():
+            for op in func.block.operations:
+                du = plan.get_compute_device_usage_for_mlprogram_operation(op)
+                dev = type(du.preferred_compute_device).__name__ if du else "None"
+                counts[dev] = counts.get(dev, 0) + 1
+        print("\nPer-op preferred device (CPU_AND_NE):")
+        for dev, n in sorted(counts.items(), key=lambda x: -x[1]):
+            print(f"  {dev:20s} {n}")
+    except Exception as e:
+        print(f"\n(MLComputePlan breakdown unavailable: {str(e)[:100]})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/models/llm/qwen3-0.6b/coreml/pyproject.toml b/models/llm/qwen3-0.6b/coreml/pyproject.toml
new file mode 100644
index 0000000..007e0b3
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/pyproject.toml
@@ -0,0 +1,16 @@
+[project]
+name = "qwen3-0.6b-coreml"
+version = "0.1.0"
+description = "CoreML conversion + on-device benchmark for standalone Qwen/Qwen3-0.6B (LLM-on-ANE thesis test)"
+requires-python = ">=3.10,<3.12"
+dependencies = [
+    "torch>=2.4",
+    "transformers>=4.51",
+    "coremltools>=9.0",
+    "numpy>=1.24,<2.0",
+    "huggingface-hub>=0.30.0",
+    "safetensors>=0.4.0",
+]
+
+[tool.uv]
+dev-dependencies = []
diff --git a/models/llm/qwen3-0.6b/coreml/uv.lock b/models/llm/qwen3-0.6b/coreml/uv.lock
new file mode 100644
index 0000000..f30873b
--- /dev/null
+++ b/models/llm/qwen3-0.6b/coreml/uv.lock
@@ -0,0 +1,896 @@
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