diff --git a/tests/integration/model_bridge/test_qwen3_moe_bridge.py b/tests/integration/model_bridge/test_qwen3_moe_bridge.py
new file mode 100644
index 000000000..e952f6fb6
--- /dev/null
+++ b/tests/integration/model_bridge/test_qwen3_moe_bridge.py
@@ -0,0 +1,173 @@
+"""Integration tests for the Qwen3MoE TransformerBridge.
+
+Uses a tiny programmatic config on the meta device — no network access or
+weight downloads. Tensor ops can't execute on meta, so forward-pass tests are
+skipped and run manually during verification. Fixture pattern mirrors
+tests/unit/model_bridge/test_gpt_oss_moe.py.
+"""
+
+import pytest
+import torch
+from transformers import AutoConfig, AutoModelForCausalLM
+
+from transformer_lens.config import TransformerBridgeConfig
+from transformer_lens.model_bridge.bridge import TransformerBridge
+from transformer_lens.model_bridge.generalized_components import MoEBridge
+from transformer_lens.model_bridge.sources.transformers import (
+    map_default_transformer_lens_config,
+)
+from transformer_lens.model_bridge.supported_architectures.qwen3_moe import (
+    Qwen3MoeArchitectureAdapter,
+)
+
+
+class _MockTokenizer:
+    """Stand-in to satisfy TransformerBridge(tokenizer=...)."""
+
+    pass
+
+
+@pytest.fixture(scope="module")
+def tiny_qwen3moe_config():
+    """Small Qwen3MoeConfig: 2 layers, 4 heads, 4 experts."""
+    return AutoConfig.for_model(
+        "qwen3_moe",
+        hidden_size=64,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        num_key_value_heads=2,
+        head_dim=16,
+        moe_intermediate_size=32,
+        num_experts=4,
+        num_experts_per_tok=2,
+        vocab_size=256,
+        max_position_embeddings=128,
+        decoder_sparse_step=1,
+        mlp_only_layers=[],
+    )
+
+
+@pytest.fixture(scope="module")
+def tiny_qwen3moe_model_meta(tiny_qwen3moe_config):
+    """Qwen3MoE model on meta device (no weights loaded)."""
+    with torch.device("meta"):
+        model = AutoModelForCausalLM.from_config(tiny_qwen3moe_config)
+    return model
+
+
+@pytest.fixture(scope="module")
+def tiny_qwen3moe_bridge(tiny_qwen3moe_config, tiny_qwen3moe_model_meta):
+    """TransformerBridge wrapping the tiny meta-device Qwen3MoE model."""
+    tl_config = map_default_transformer_lens_config(tiny_qwen3moe_config)
+
+    bridge_config = TransformerBridgeConfig(
+        d_model=tl_config.d_model,
+        d_head=tl_config.d_head,
+        n_layers=tl_config.n_layers,
+        n_ctx=tl_config.n_ctx,
+        n_heads=tl_config.n_heads,
+        n_key_value_heads=tl_config.n_key_value_heads,
+        d_vocab=tl_config.d_vocab,
+        architecture="Qwen3MoeForCausalLM",
+    )
+
+    adapter = Qwen3MoeArchitectureAdapter(bridge_config)
+
+    return TransformerBridge(
+        model=tiny_qwen3moe_model_meta,
+        adapter=adapter,
+        tokenizer=_MockTokenizer(),
+    )
+
+
+class TestQwen3MoeModelStructure:
+    def test_model_has_layers(self, tiny_qwen3moe_model_meta) -> None:
+        assert hasattr(tiny_qwen3moe_model_meta, "model")
+        assert hasattr(tiny_qwen3moe_model_meta.model, "layers")
+        assert len(tiny_qwen3moe_model_meta.model.layers) == 2
+
+    def test_layer_has_sparse_moe_block(self, tiny_qwen3moe_model_meta) -> None:
+        # Qwen3MoeSparseMoeBlock stores experts as batched 3D tensors, not a ModuleList
+        layer0_mlp = tiny_qwen3moe_model_meta.model.layers[0].mlp
+        assert hasattr(layer0_mlp, "experts")
+        experts = layer0_mlp.experts
+        assert hasattr(experts, "gate_up_proj")
+        assert hasattr(experts, "down_proj")
+        assert not hasattr(experts, "__iter__")
+
+    def test_layer_has_gate_router(self, tiny_qwen3moe_model_meta) -> None:
+        layer0_mlp = tiny_qwen3moe_model_meta.model.layers[0].mlp
+        assert hasattr(layer0_mlp, "gate")
+
+    def test_attention_has_q_norm_k_norm(self, tiny_qwen3moe_model_meta) -> None:
+        attn = tiny_qwen3moe_model_meta.model.layers[0].self_attn
+        assert hasattr(attn, "q_norm")
+        assert hasattr(attn, "k_norm")
+
+
+class TestQwen3MoeBridgeStructure:
+    def test_block_count(self, tiny_qwen3moe_bridge) -> None:
+        assert len(tiny_qwen3moe_bridge.blocks) == 2
+
+    def test_has_core_components(self, tiny_qwen3moe_bridge) -> None:
+        assert hasattr(tiny_qwen3moe_bridge, "embed")
+        assert hasattr(tiny_qwen3moe_bridge, "unembed")
+        assert hasattr(tiny_qwen3moe_bridge, "ln_final")
+
+    def test_cfg_final_rms_is_true(self, tiny_qwen3moe_bridge) -> None:
+        """Qwen3MoE uses final_rms=True; OLMoE uses False."""
+        assert tiny_qwen3moe_bridge.cfg.final_rms is True
+
+    def test_cfg_n_kv_heads(self, tiny_qwen3moe_bridge) -> None:
+        assert tiny_qwen3moe_bridge.cfg.n_key_value_heads == 2
+
+    def test_cfg_positional_embedding_type(self, tiny_qwen3moe_bridge) -> None:
+        assert tiny_qwen3moe_bridge.cfg.positional_embedding_type == "rotary"
+
+    def test_cfg_normalization_type(self, tiny_qwen3moe_bridge) -> None:
+        assert tiny_qwen3moe_bridge.cfg.normalization_type == "RMS"
+
+    def test_mlp_blocks_are_moe_bridge(self, tiny_qwen3moe_bridge) -> None:
+        for i, block in enumerate(tiny_qwen3moe_bridge.blocks):
+            assert isinstance(
+                block.mlp, MoEBridge
+            ), f"Block {i} mlp is {type(block.mlp).__name__}, expected MoEBridge"
+
+    def test_moe_bridge_has_router_scores_hook(self, tiny_qwen3moe_bridge) -> None:
+        mlp = tiny_qwen3moe_bridge.blocks[0].mlp
+        assert hasattr(mlp, "hook_router_scores")
+
+    def test_block_has_ln1_and_ln2(self, tiny_qwen3moe_bridge) -> None:
+        block = tiny_qwen3moe_bridge.blocks[0]
+        assert hasattr(block, "ln1")
+        assert hasattr(block, "ln2")
+
+    def test_block_attn_has_q_norm_k_norm(self, tiny_qwen3moe_bridge) -> None:
+        attn = tiny_qwen3moe_bridge.blocks[0].attn
+        assert hasattr(attn, "q_norm")
+        assert hasattr(attn, "k_norm")
+
+
+# Forward-pass tests require real weights — meta-device tensor ops raise
+# NotImplementedError. Run these manually during Step 3 verification.
+
+
+@pytest.mark.skip(reason="Requires real weights — run manually during verification")
+def test_forward_pass_matches_hf(tiny_qwen3moe_bridge) -> None:
+    """Bridge logits match the HF model."""
+    tokens = torch.tensor([[1, 2, 3, 4]])
+    with torch.no_grad():
+        bridge_out = tiny_qwen3moe_bridge(tokens)
+        hf_out = tiny_qwen3moe_bridge.original_model(tokens).logits
+    max_diff = (bridge_out - hf_out).abs().max().item()
+    assert max_diff < 1e-4, f"Bridge vs HF max diff = {max_diff}"
+
+
+@pytest.mark.skip(reason="Requires real weights — run manually during verification")
+def test_run_with_cache_captures_moe_router_scores(tiny_qwen3moe_bridge) -> None:
+    """MoEBridge captures router scores in the activation cache."""
+    tiny_qwen3moe_bridge.enable_compatibility_mode(no_processing=True)
+    tokens = torch.tensor([[1, 2, 3, 4]])
+    _, cache = tiny_qwen3moe_bridge.run_with_cache(tokens)
+    for i in range(len(tiny_qwen3moe_bridge.blocks)):
+        assert f"blocks.{i}.mlp.hook_router_scores" in cache, f"Missing router scores for block {i}"
diff --git a/tests/unit/model_bridge/test_qwen3_moe_adapter.py b/tests/unit/model_bridge/test_qwen3_moe_adapter.py
new file mode 100644
index 000000000..af6a0155c
--- /dev/null
+++ b/tests/unit/model_bridge/test_qwen3_moe_adapter.py
@@ -0,0 +1,194 @@
+"""Unit tests for the Qwen3MoeArchitectureAdapter.
+
+All tests use programmatic TransformerBridgeConfig instances — no network access
+or model downloads.
+"""
+
+import pytest
+
+from transformer_lens.config import TransformerBridgeConfig
+from transformer_lens.conversion_utils.conversion_steps.rearrange_tensor_conversion import (
+    RearrangeTensorConversion,
+)
+from transformer_lens.conversion_utils.param_processing_conversion import (
+    ParamProcessingConversion,
+)
+from transformer_lens.factories.architecture_adapter_factory import (
+    SUPPORTED_ARCHITECTURES,
+)
+from transformer_lens.model_bridge.generalized_components import (
+    MoEBridge,
+    RMSNormalizationBridge,
+)
+from transformer_lens.model_bridge.supported_architectures.qwen3_moe import (
+    Qwen3MoeArchitectureAdapter,
+)
+
+
+@pytest.fixture
+def cfg() -> TransformerBridgeConfig:
+    return TransformerBridgeConfig(
+        d_model=64,
+        d_head=16,
+        n_layers=2,
+        n_ctx=128,
+        n_heads=4,
+        n_key_value_heads=2,
+        d_vocab=256,
+        architecture="Qwen3MoeForCausalLM",
+    )
+
+
+@pytest.fixture
+def adapter(cfg: TransformerBridgeConfig) -> Qwen3MoeArchitectureAdapter:
+    return Qwen3MoeArchitectureAdapter(cfg)
+
+
+class TestQwen3MoeAdapterConfig:
+    def test_normalization_type_is_rms(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        assert adapter.cfg.normalization_type == "RMS"
+
+    def test_positional_embedding_type_is_rotary(
+        self, adapter: Qwen3MoeArchitectureAdapter
+    ) -> None:
+        assert adapter.cfg.positional_embedding_type == "rotary"
+
+    def test_final_rms_is_true(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        """Qwen3MoE uses final_rms=True; OLMoE uses False."""
+        assert adapter.cfg.final_rms is True
+
+    def test_gated_mlp_is_true(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        assert adapter.cfg.gated_mlp is True
+
+    def test_uses_rms_norm_is_true(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        assert adapter.cfg.uses_rms_norm is True
+
+    def test_attn_implementation_is_eager(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        assert adapter.cfg.attn_implementation == "eager"
+
+    def test_default_prepend_bos_is_false(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        assert adapter.cfg.default_prepend_bos is False
+
+    def test_n_kv_heads_propagated(self) -> None:
+        """n_key_value_heads from the loaded config is preserved."""
+        cfg = TransformerBridgeConfig(
+            d_model=64,
+            d_head=16,
+            n_layers=2,
+            n_ctx=128,
+            n_heads=4,
+            n_key_value_heads=2,
+            d_vocab=256,
+            architecture="Qwen3MoeForCausalLM",
+        )
+        adapter = Qwen3MoeArchitectureAdapter(cfg)
+        assert adapter.cfg.n_key_value_heads == 2
+
+
+class TestQwen3MoeWeightConversions:
+    def test_has_qkvo_keys(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        convs = adapter.weight_processing_conversions
+        assert convs is not None
+        assert "blocks.{i}.attn.q.weight" in convs
+        assert "blocks.{i}.attn.k.weight" in convs
+        assert "blocks.{i}.attn.v.weight" in convs
+        assert "blocks.{i}.attn.o.weight" in convs
+
+    def test_q_rearrange_uses_n_heads(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        """Q rearrange uses n_heads (4)."""
+        convs = adapter.weight_processing_conversions
+        assert convs is not None
+        q_conv = convs["blocks.{i}.attn.q.weight"]
+        assert isinstance(q_conv, ParamProcessingConversion)
+        assert isinstance(q_conv.tensor_conversion, RearrangeTensorConversion)
+        axes = q_conv.tensor_conversion.axes_lengths
+        assert axes.get("n") == 4
+
+    def test_kv_rearrange_uses_n_kv_heads(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        """K/V rearrange uses n_key_value_heads (2) for GQA."""
+        convs = adapter.weight_processing_conversions
+        assert convs is not None
+        k_conv = convs["blocks.{i}.attn.k.weight"]
+        v_conv = convs["blocks.{i}.attn.v.weight"]
+        assert isinstance(k_conv, ParamProcessingConversion)
+        assert isinstance(v_conv, ParamProcessingConversion)
+        assert isinstance(k_conv.tensor_conversion, RearrangeTensorConversion)
+        assert isinstance(v_conv.tensor_conversion, RearrangeTensorConversion)
+        assert k_conv.tensor_conversion.axes_lengths.get("n") == 2
+        assert v_conv.tensor_conversion.axes_lengths.get("n") == 2
+
+    def test_o_rearrange_uses_n_heads(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        """O rearrange uses n_heads (4)."""
+        convs = adapter.weight_processing_conversions
+        assert convs is not None
+        o_conv = convs["blocks.{i}.attn.o.weight"]
+        assert isinstance(o_conv, ParamProcessingConversion)
+        assert isinstance(o_conv.tensor_conversion, RearrangeTensorConversion)
+        assert o_conv.tensor_conversion.axes_lengths.get("n") == 4
+
+
+class TestQwen3MoeComponentMapping:
+    def test_has_required_top_level_keys(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        for key in ("embed", "rotary_emb", "blocks", "ln_final", "unembed"):
+            assert key in mapping, f"Missing top-level key: {key!r}"
+
+    def test_blocks_has_required_submodules(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        blocks = mapping["blocks"]
+        for key in ("ln1", "ln2", "attn", "mlp"):
+            assert key in blocks.submodules, f"Missing blocks submodule: {key!r}"
+
+    def test_attn_has_all_submodules(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        attn = mapping["blocks"].submodules["attn"]
+        for key in ("q", "k", "v", "o", "q_norm", "k_norm"):
+            assert key in attn.submodules, f"Missing attn submodule: {key!r}"
+
+    def test_ln1_ln2_are_rms_norm_bridges(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        subs = mapping["blocks"].submodules
+        assert isinstance(subs["ln1"], RMSNormalizationBridge)
+        assert isinstance(subs["ln2"], RMSNormalizationBridge)
+
+    def test_mlp_is_moe_bridge(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        mlp = mapping["blocks"].submodules["mlp"]
+        assert isinstance(mlp, MoEBridge)
+
+    def test_mlp_has_gate_submodule(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        mlp = mapping["blocks"].submodules["mlp"]
+        assert "gate" in mlp.submodules
+
+    def test_q_norm_k_norm_are_rms_norm_bridges(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        attn_subs = mapping["blocks"].submodules["attn"].submodules
+        assert isinstance(attn_subs["q_norm"], RMSNormalizationBridge)
+        assert isinstance(attn_subs["k_norm"], RMSNormalizationBridge)
+
+    def test_hf_module_paths(self, adapter: Qwen3MoeArchitectureAdapter) -> None:
+        """HF module path names are mapped correctly."""
+        mapping = adapter.component_mapping
+        assert mapping is not None
+        assert mapping["embed"].name == "model.embed_tokens"
+        assert mapping["ln_final"].name == "model.norm"
+        assert mapping["unembed"].name == "lm_head"
+        assert mapping["blocks"].name == "model.layers"
+        subs = mapping["blocks"].submodules
+        assert subs["ln1"].name == "input_layernorm"
+        assert subs["ln2"].name == "post_attention_layernorm"
+        assert subs["attn"].name == "self_attn"
+        assert subs["mlp"].name == "mlp"
+
+
+class TestQwen3MoeFactoryRegistration:
+    def test_factory_lookup_returns_adapter_class(self) -> None:
+        assert SUPPORTED_ARCHITECTURES["Qwen3MoeForCausalLM"] is Qwen3MoeArchitectureAdapter
diff --git a/transformer_lens/factories/architecture_adapter_factory.py b/transformer_lens/factories/architecture_adapter_factory.py
index 01e0c59b7..eb62e4f0d 100644
--- a/transformer_lens/factories/architecture_adapter_factory.py
+++ b/transformer_lens/factories/architecture_adapter_factory.py
@@ -44,6 +44,7 @@
     PhiArchitectureAdapter,
     Qwen2ArchitectureAdapter,
     Qwen3ArchitectureAdapter,
+    Qwen3MoeArchitectureAdapter,
     Qwen3NextArchitectureAdapter,
     QwenArchitectureAdapter,
     StableLmArchitectureAdapter,
@@ -91,6 +92,7 @@
     "QwenForCausalLM": QwenArchitectureAdapter,
     "Qwen2ForCausalLM": Qwen2ArchitectureAdapter,
     "Qwen3ForCausalLM": Qwen3ArchitectureAdapter,
+    "Qwen3MoeForCausalLM": Qwen3MoeArchitectureAdapter,
     "Qwen3NextForCausalLM": Qwen3NextArchitectureAdapter,
     "StableLmForCausalLM": StableLmArchitectureAdapter,
     "T5ForConditionalGeneration": T5ArchitectureAdapter,
diff --git a/transformer_lens/model_bridge/supported_architectures/__init__.py b/transformer_lens/model_bridge/supported_architectures/__init__.py
index c2d5510ad..a63f82abc 100644
--- a/transformer_lens/model_bridge/supported_architectures/__init__.py
+++ b/transformer_lens/model_bridge/supported_architectures/__init__.py
@@ -123,6 +123,9 @@
 from transformer_lens.model_bridge.supported_architectures.qwen3 import (
     Qwen3ArchitectureAdapter,
 )
+from transformer_lens.model_bridge.supported_architectures.qwen3_moe import (
+    Qwen3MoeArchitectureAdapter,
+)
 from transformer_lens.model_bridge.supported_architectures.qwen3_next import (
     Qwen3NextArchitectureAdapter,
 )
@@ -174,6 +177,7 @@
     "QwenArchitectureAdapter",
     "Qwen2ArchitectureAdapter",
     "Qwen3ArchitectureAdapter",
+    "Qwen3MoeArchitectureAdapter",
     "Qwen3NextArchitectureAdapter",
     "StableLmArchitectureAdapter",
     "T5ArchitectureAdapter",
diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py b/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py
new file mode 100644
index 000000000..643892cee
--- /dev/null
+++ b/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py
@@ -0,0 +1,144 @@
+"""Qwen3MoE (Mixture of Experts) architecture adapter."""
+
+from typing import Any
+
+from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
+from transformer_lens.model_bridge.generalized_components import (
+    BlockBridge,
+    EmbeddingBridge,
+    LinearBridge,
+    MoEBridge,
+    PositionEmbeddingsAttentionBridge,
+    RMSNormalizationBridge,
+    RotaryEmbeddingBridge,
+    UnembeddingBridge,
+)
+
+
+class Qwen3MoeArchitectureAdapter(ArchitectureAdapter):
+    """Architecture adapter for Qwen3MoE (Mixture of Experts) models.
+
+    Qwen3MoE is a sparse MoE decoder-only Transformer, structurally close to OLMoE.
+    Key features:
+
+    - Pre-norm: RMSNorm applied BEFORE attention and BEFORE MLP.
+    - Q/K normalization: RMSNorm applied to queries and keys after projection.
+    - Sparse MoE: 128 experts with top-8 routing (public 30B-A3B checkpoints).
+    - Batched expert parameters: gate_up_proj and down_proj as single 3D tensors,
+      not a ModuleList.
+    - final_rms=True (Qwen3-style; OLMoE uses False).
+    - No biases on any projections.
+    - GQA: n_key_value_heads < n_heads in all public checkpoints.
+
+    Only the all-MoE configuration is supported (decoder_sparse_step=1,
+    mlp_only_layers=[]). Models with dense fallback layers cannot be wrapped
+    because MoEBridge does not handle the dense Qwen3MoeMLP path.
+
+    Optional Parameters (may not exist in state_dict):
+    -------------------------------------------------
+    - blocks.{i}.attn.b_Q - No bias on query projection
+    - blocks.{i}.attn.b_K - No bias on key projection
+    - blocks.{i}.attn.b_V - No bias on value projection
+    - blocks.{i}.attn.b_O - No bias on output projection
+    - blocks.{i}.ln1.b - RMSNorm has no bias
+    - blocks.{i}.ln2.b - RMSNorm has no bias
+    - ln_final.b - RMSNorm has no bias
+    """
+
+    def __init__(self, cfg: Any) -> None:
+        """Initialize the Qwen3MoE architecture adapter."""
+        super().__init__(cfg)
+
+        # Set config variables for weight processing
+        self.cfg.normalization_type = "RMS"
+        self.cfg.positional_embedding_type = "rotary"
+        self.cfg.final_rms = True  # Qwen3-style; OLMoE uses False
+        self.cfg.gated_mlp = True
+        self.cfg.attn_only = False
+        self.cfg.uses_rms_norm = True
+        # Force eager attention for output_attentions hook support
+        self.cfg.attn_implementation = "eager"
+        self.cfg.default_prepend_bos = False  # Qwen3 family convention
+
+        # GQA support
+        if hasattr(cfg, "n_key_value_heads") and cfg.n_key_value_heads is not None:
+            self.cfg.n_key_value_heads = cfg.n_key_value_heads
+
+        # QKVO rearrangements; MoE expert and gate weights pass through unchanged
+        self.weight_processing_conversions = {
+            **self._qkvo_weight_conversions(),
+        }
+
+        # Component mapping — PRE-NORM architecture:
+        # ln1 = input_layernorm (applied BEFORE attention)
+        # ln2 = post_attention_layernorm (applied BEFORE MLP)
+        self.component_mapping = {
+            "embed": EmbeddingBridge(name="model.embed_tokens"),
+            "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg),
+            "blocks": BlockBridge(
+                name="model.layers",
+                submodules={
+                    "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
+                    "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
+                    "attn": PositionEmbeddingsAttentionBridge(
+                        name="self_attn",
+                        config=self.cfg,
+                        submodules={
+                            "q": LinearBridge(name="q_proj"),
+                            "k": LinearBridge(name="k_proj"),
+                            "v": LinearBridge(name="v_proj"),
+                            "o": LinearBridge(name="o_proj"),
+                            "q_norm": RMSNormalizationBridge(name="q_norm", config=self.cfg),
+                            "k_norm": RMSNormalizationBridge(name="k_norm", config=self.cfg),
+                        },
+                        requires_attention_mask=True,
+                        requires_position_embeddings=True,
+                    ),
+                    # Qwen3MoeSparseMoeBlock stores experts as batched 3D tensors
+                    # rather than a ModuleList. MoEBridge wraps the entire block and
+                    # delegates to HF's native forward. The gate (router) is mapped
+                    # as a submodule for hook access — same pattern as OLMoE.
+                    "mlp": MoEBridge(
+                        name="mlp",
+                        config=self.cfg,
+                        submodules={
+                            "gate": LinearBridge(name="gate"),
+                        },
+                    ),
+                },
+            ),
+            "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
+            "unembed": UnembeddingBridge(name="lm_head", config=self.cfg),
+        }
+
+    def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
+        """Set up rotary embedding references for Qwen3MoE component testing.
+
+        Qwen3MoE uses RoPE (Rotary Position Embeddings). We set the rotary_emb
+        reference on all attention bridge instances for component testing.
+
+        Args:
+            hf_model: The HuggingFace Qwen3MoE model instance
+            bridge_model: The TransformerBridge model (if available)
+        """
+        # Get rotary embedding instance from the model
+        rotary_emb = hf_model.model.rotary_emb
+
+        # Force HF model to use "eager" attention to match bridge implementation
+        if hasattr(hf_model, "config") and hasattr(hf_model.config, "_attn_implementation"):
+            hf_model.config._attn_implementation = "eager"
+
+        if hasattr(hf_model, "model") and hasattr(hf_model.model, "layers"):
+            for layer in hf_model.model.layers:
+                if hasattr(layer, "self_attn") and hasattr(layer.self_attn, "config"):
+                    layer.self_attn.config._attn_implementation = "eager"
+
+        # Set rotary_emb on actual bridge instances in bridge_model if available
+        if bridge_model is not None and hasattr(bridge_model, "blocks"):
+            for block in bridge_model.blocks:
+                if hasattr(block, "attn"):
+                    block.attn.set_rotary_emb(rotary_emb)
+
+        # Also set on the template for get_generalized_component() calls
+        attn_bridge = self.get_generalized_component("blocks.0.attn")
+        attn_bridge.set_rotary_emb(rotary_emb)
diff --git a/transformer_lens/tools/model_registry/data/supported_models.json b/transformer_lens/tools/model_registry/data/supported_models.json
index 2d8a67999..531437391 100644
--- a/transformer_lens/tools/model_registry/data/supported_models.json
+++ b/transformer_lens/tools/model_registry/data/supported_models.json
@@ -6,9 +6,9 @@
     "min_downloads": 500,
     "scan_duration_seconds": 0.0
   },
-  "total_architectures": 37,
-  "total_models": 5563,
-  "total_verified": 693,
+  "total_architectures": 38,
+  "total_models": 6822,
+  "total_verified": 698,
   "models": [
     {
       "architecture_id": "Qwen3NextForCausalLM",
@@ -77820,6 +77820,20 @@
       "phase4_score": 89.9,
       "phase7_score": null
     },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-30B-A3B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
     {
       "architecture_id": "Qwen2ForCausalLM",
       "model_id": "xw1234gan/Extended_GRPO_KL_Qwen2.5-3B-Instruct_MATH_beta0_lr1e-05_mb2_ga128_n2048_seed42",
@@ -77848,6 +77862,34 @@
       "phase7_score": null,
       "phase8_score": null
     },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-30B-A3B-Instruct-2507",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
     {
       "architecture_id": "LlamaForCausalLM",
       "model_id": "Yukang/Llama-2-13b-longlora-16k-ft",
@@ -77862,6 +77904,20 @@
       "phase7_score": null,
       "phase8_score": null
     },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-30B-A3B-Thinking-2507",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
     {
       "architecture_id": "LlamaForCausalLM",
       "model_id": "bhenrym14/airoboros-3_1-yi-34b-200k",
@@ -93751,6 +93807,2165 @@
       "phase4_score": 67.5,
       "phase7_score": null,
       "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-235B-A22B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 594767,
+        "total_params": 235093634560
+      },
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-30B-A3B-Instruct-2507-FP8",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 370376,
+        "total_params": 30533947392
+      },
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 354610,
+        "total_params": 30533947392
+      },
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+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
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+      "model_id": "trl-internal-testing/tiny-Qwen3MoeForCausalLM",
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diff --git a/transformer_lens/tools/model_registry/data/supported_models_qwen3_moe.json b/transformer_lens/tools/model_registry/data/supported_models_qwen3_moe.json
new file mode 100644
index 000000000..cd5b8e104
--- /dev/null
+++ b/transformer_lens/tools/model_registry/data/supported_models_qwen3_moe.json
@@ -0,0 +1,2200 @@
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
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+    },
+    {
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+      "status": 0,
+      "verified_date": null,
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+    },
+    {
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+      "model_id": "nightmedia/Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER-qx4-mlx",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "mlx-community/Qwen3-235B-A22B-Thinking-2507-8bit",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
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+        "total_params": 235093634560
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "baichuan-inc/Baichuan-M3-235B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 737,
+        "total_params": 235093634560
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "huihui-ai/Huihui-MiroThinker-v1.5-30B-abliterated",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
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+        "total_params": 30532122624
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+      "phase8_score": null
+    },
+    {
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+      "model_id": "unsloth/Qwen3-30B-A3B-Base",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 726,
+        "total_params": null
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+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "Jebadiah/Qwen3-30B-A3B-seed-0",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 719,
+        "total_params": 30531028992
+      },
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "DavidAU/Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 717,
+        "total_params": 42371414784
+      },
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+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "nytopop/Qwen3-30B-A3B.w4a16",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 691,
+        "total_params": 4605856128
+      },
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+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "tokyotech-llm/Qwen3-Swallow-30B-A3B-RL-v0.2",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 677,
+        "total_params": 30532122624
+      },
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "lmstudio-community/Qwen3-235B-A22B-Thinking-2507-MLX-8bit",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 673,
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+      },
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+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "nightmedia/Qwen3-30B-A3B-Thinking-2507-Claude-4.5-Sonnet-High-Reasoning-Distill-mxfp4-mlx",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 669,
+        "total_params": 30532122624
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+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "RedHatAI/Qwen3-30B-A3B-Instruct-2507-quantized.w4a16",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 663,
+        "total_params": 4594944480
+      },
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+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "RedHatAI/Qwen3-235B-A22B-Instruct-2507-NVFP4",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 658,
+        "total_params": 135739033600
+      },
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "model_id": "unsloth/Qwen3-30B-A3B-Instruct-2507-FP8",
+      "status": 0,
+      "verified_date": null,
+      "metadata": {
+        "downloads": 647,
+        "total_params": null
+      },
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    }
+  ]
+}
\ No newline at end of file
diff --git a/transformer_lens/tools/model_registry/data/verification_history.json b/transformer_lens/tools/model_registry/data/verification_history.json
index 920affc5c..a701f2d19 100644
--- a/transformer_lens/tools/model_registry/data/verification_history.json
+++ b/transformer_lens/tools/model_registry/data/verification_history.json
@@ -1,5 +1,5 @@
 {
-  "last_updated": "2026-04-10T00:51:34.188066",
+  "last_updated": "2026-04-10T15:08:21.930188",
   "records": [
     {
       "model_id": "Macropodus/macbert4mdcspell_v1",
@@ -11201,6 +11201,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "trl-internal-testing/tiny-Qwen3MoeForCausalLM",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P3=89.5% but required tests failed: logits_equivalence \u2014 Text quality score: 72.2/100 (avg perplexity: 558.8) \u2014 generated text may be incoherent",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "tiny-random/qwen3-next-moe",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11211,6 +11221,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "huihui-ai/Huihui-MoE-0.8B-2E",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P3=89.5% but required tests failed: logits_equivalence, loss_equivalence \u2014 Tensors differ: max_diff=0.625000, mean_rel=0.002930",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "optimum-intel-internal-testing/tiny-random-qwen3-next",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11221,6 +11241,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "huihui-ai/Huihui-MoE-0.8B-2E",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P3=89.5% but required tests failed: logits_equivalence, loss_equivalence \u2014 Tensors differ: max_diff=0.625000, mean_rel=0.002930",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "yujiepan/qwen3-next-moe-tiny-random",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11231,6 +11261,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "huihui-ai/Huihui-MoE-0.8B-2E",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P3=89.5% but required tests failed: logits_equivalence, loss_equivalence \u2014 Tensors differ: max_diff=0.625000, mean_rel=0.002930",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "tiny-random/qwen3-next-moe",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11241,6 +11281,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "huihui-ai/Huihui-MoE-0.8B-2E",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "optimum-intel-internal-testing/tiny-random-qwen3-next",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11271,6 +11321,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "huihui-ai/Huihui-MoE-1B-A0.6B",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "tiny-random/qwen3-next-moe",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11281,6 +11341,16 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "imdatta0/tiny_qwen3_moe_2.8B_0.7B",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "optimum-intel-internal-testing/tiny-random-qwen3-next",
       "architecture_id": "Qwen3NextForCausalLM",
@@ -11291,6 +11361,26 @@
       "invalidated": false,
       "invalidation_reason": null
     },
+    {
+      "model_id": "yujiepan/qwen3-moe-tiny-random",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'Qwen3MoeMLP' object has no attribute 'gate'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-internal-testing/tiny-random-qwen3_moe",
+      "architecture_id": "Qwen3MoeForCausalLM",
+      "verified_date": "2026-04-10",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: 'Qwen3MoeMLP' object has no attribute 'gate'",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
     {
       "model_id": "yujiepan/qwen3-next-moe-tiny-random",
       "architecture_id": "Qwen3NextForCausalLM",
diff --git a/transformer_lens/tools/model_registry/verify_models.py b/transformer_lens/tools/model_registry/verify_models.py
index a31e91a63..0aecc8eba 100644
--- a/transformer_lens/tools/model_registry/verify_models.py
+++ b/transformer_lens/tools/model_registry/verify_models.py
@@ -249,6 +249,7 @@ def estimate_model_params(model_id: str) -> int:
                     "mixtral",
                     "qwen2",
                     "qwen3",
+                    "qwen3_moe",
                     "phi3",
                     "stablelm",
                 )
@@ -262,8 +263,11 @@ def estimate_model_params(model_id: str) -> int:
             lang_config, "num_experts", None
         )
         if num_experts and num_experts > 1:
-            # For MoE, MLP params are multiplied by num_experts + gate params
-            mlp_per_layer = d_model * d_mlp * mlp_multiplier
+            # Qwen3MoE and similar store per-expert hidden size in moe_intermediate_size;
+            # intermediate_size refers to a dense fallback MLP that we don't use here.
+            moe_d_mlp = getattr(lang_config, "moe_intermediate_size", None) or d_mlp
+            # MLP params scale with num_experts; add gate params per expert
+            mlp_per_layer = d_model * moe_d_mlp * mlp_multiplier
             moe_per_layer = (mlp_per_layer + d_model) * num_experts
             # Replace the non-MoE MLP contribution
             n_params -= n_layers * (d_model * d_mlp * mlp_multiplier)
@@ -279,13 +283,15 @@ def estimate_benchmark_memory_gb(
     n_params: int,
     dtype: str = "float32",
     phases: Optional[list[int]] = None,
+    use_hf_reference: bool = True,
 ) -> float:
     """Estimate peak memory needed for benchmark suite.
 
     Phases run sequentially, so peak memory is the maximum of any single phase,
     not the sum. The multiplier represents how many model copies exist at peak:
 
-    Phase 1: Briefly loads HF ref + Bridge → 2.0x peak
+    Phase 1 (HF ref on):  HF ref + Bridge → 2.0x peak
+    Phase 1 (HF ref off): Bridge only     → 1.0x peak
     Phase 2: Bridge + HookedTransformer (separate copy) → 2.0x model + overhead
     Phase 3: Same as Phase 2 (processed versions) → 2.0x model + overhead
     Phase 4: Bridge + GPT-2 scorer (~500MB) → ~1.0x model + 0.5 GB
@@ -294,6 +300,8 @@ def estimate_benchmark_memory_gb(
         n_params: Number of model parameters
         dtype: Data type for memory calculation
         phases: Which phases will be run (None = all phases)
+        use_hf_reference: Whether Phase 1 loads an HF reference alongside the
+            Bridge. Mirrors the ``--no-hf-reference`` CLI flag.
 
     Returns:
         Estimated peak memory in GB
@@ -315,9 +323,12 @@ def estimate_benchmark_memory_gb(
         phases = [1, 2, 3, 4]
 
     for p in phases:
-        if p in (1, 2, 3):
-            # Phase 1: HF ref + Bridge = 2 copies briefly
-            # Phase 2/3: Bridge + HookedTransformer = 2 copies
+        if p == 1:
+            # HF ref + Bridge (2 copies) or Bridge alone
+            multiplier = 2.0 if use_hf_reference else 1.0
+            phase_peaks.append(model_size_gb * multiplier * (1 + overhead_fraction))
+        elif p in (2, 3):
+            # Bridge + HookedTransformer = 2 copies
             phase_peaks.append(model_size_gb * 2.0 * (1 + overhead_fraction))
         elif p == 4:
             # Bridge + GPT-2 scorer
@@ -781,7 +792,9 @@ def verify_models(
             continue
 
         # Step 2: Check memory
-        estimated_mem = estimate_benchmark_memory_gb(n_params, dtype, phases=phases)
+        estimated_mem = estimate_benchmark_memory_gb(
+            n_params, dtype, phases=phases, use_hf_reference=use_hf_reference
+        )
         candidate.estimated_memory_gb = estimated_mem
         if not quiet:
             print(
@@ -1087,6 +1100,7 @@ def _print_dry_run(
     dtype: str,
     max_memory_gb: float,
     phases: Optional[list[int]] = None,
+    use_hf_reference: bool = True,
 ) -> None:
     """Print what would be tested in a dry run."""
     print(f"\nDry run: {len(candidates)} models would be tested")
@@ -1107,7 +1121,9 @@ def _print_dry_run(
         for c in models:
             try:
                 n_params = estimate_model_params(c.model_id)
-                mem = estimate_benchmark_memory_gb(n_params, dtype, phases=phases)
+                mem = estimate_benchmark_memory_gb(
+                    n_params, dtype, phases=phases, use_hf_reference=use_hf_reference
+                )
                 status = "OK" if mem <= max_memory_gb else "SKIP (too large)"
                 if mem > max_memory_gb:
                     skippable += 1
@@ -1339,6 +1355,7 @@ def main() -> None:
             args.dtype,
             max_memory_gb,
             phases=args.phases,
+            use_hf_reference=not args.no_hf_reference,
         )
         return