diff --git a/examples/models/eagle3/draft.py b/examples/models/eagle3/draft.py
index c372730b784..0f7ba817759 100644
--- a/examples/models/eagle3/draft.py
+++ b/examples/models/eagle3/draft.py
@@ -45,6 +45,7 @@ class Eagle3Config:
     norm_before_residual: bool = True
     norm_before_fc: bool = False
     has_own_embed: bool = False
+    max_seq_len: int = 4096
 
 
 def _rotate_half(x: torch.Tensor) -> torch.Tensor:
@@ -52,6 +53,48 @@ def _rotate_half(x: torch.Tensor) -> torch.Tensor:
     return torch.cat((-x2, x1), dim=-1)
 
 
+class Eagle3KVCache(nn.Module):
+    """Flat KV cache for the single EAGLE-3 draft decoder layer.
+
+    ``update`` writes the new K/V at ``input_pos`` and returns the whole buffer;
+    an explicit causal mask (built by the draft) selects the valid positions, so
+    the same path serves both prefill (T>1) and single-step draft decode (T=1).
+    """
+
+    def __init__(
+        self,
+        max_batch_size: int,
+        max_seq_len: int,
+        num_kv_heads: int,
+        head_dim: int,
+    ):
+        super().__init__()
+        shape = (max_batch_size, num_kv_heads, max_seq_len, head_dim)
+        self.register_buffer("k_cache", torch.zeros(shape), persistent=False)
+        self.register_buffer("v_cache", torch.zeros(shape), persistent=False)
+
+    def update(
+        self, input_pos: torch.Tensor, k_val: torch.Tensor, v_val: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        self.k_cache.index_copy_(2, input_pos, k_val)
+        self.v_cache.index_copy_(2, input_pos, v_val)
+        return self.k_cache, self.v_cache
+
+    def allocate(self, dtype: torch.dtype, device) -> None:
+        """Re-register the cache buffers with a given dtype/device (zeroed)."""
+        shape = self.k_cache.shape
+        self.register_buffer(
+            "k_cache", torch.zeros(shape, dtype=dtype, device=device), persistent=False
+        )
+        self.register_buffer(
+            "v_cache", torch.zeros(shape, dtype=dtype, device=device), persistent=False
+        )
+
+    def reset(self) -> None:
+        self.k_cache.zero_()
+        self.v_cache.zero_()
+
+
 class Eagle3Attention(nn.Module):
     """Llama GQA attention; q/k/v project from the doubled-width (2*hidden) input."""
 
@@ -75,7 +118,16 @@ def __init__(self, config: Eagle3Config):
         )
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
-    def forward(self, x: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
+        self.kv_cache = Eagle3KVCache(
+            max_batch_size=1,
+            max_seq_len=config.max_seq_len,
+            num_kv_heads=self.n_kv_heads,
+            head_dim=self.head_dim,
+        )
+
+    def _project_rope(
+        self, x: torch.Tensor, positions: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         B, T, _ = x.shape
         q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
         k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
@@ -87,8 +139,30 @@ def forward(self, x: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
         sin = emb.sin().to(q.dtype)
         q = q * cos + _rotate_half(q) * sin
         k = k * cos + _rotate_half(k) * sin
+        return q, k, v
 
-        y = F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=True)
+    def forward(
+        self,
+        x: torch.Tensor,
+        positions: torch.Tensor,
+        attn_mask: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """Causal attention.
+
+        With ``attn_mask=None`` runs stateless over the full sequence
+        (``is_causal``). With an explicit mask, K/V are written to the KV cache
+        at ``positions`` and read back, so the same call serves prefill and
+        single-step draft decode.
+        """
+        B, T, _ = x.shape
+        q, k, v = self._project_rope(x, positions)
+        if attn_mask is None:
+            y = F.scaled_dot_product_attention(q, k, v, is_causal=True, enable_gqa=True)
+        else:
+            k, v = self.kv_cache.update(positions, k, v)
+            y = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=attn_mask, enable_gqa=True
+            )
         y = y.transpose(1, 2).contiguous().view(B, T, self.n_heads * self.head_dim)
         return self.o_proj(y)
 
@@ -129,12 +203,13 @@ def forward(
         input_embeds: torch.Tensor,
         feature: torch.Tensor,
         positions: torch.Tensor,
+        attn_mask: torch.Tensor = None,
     ) -> torch.Tensor:
         normed_embeds = self.input_layernorm(input_embeds)
         normed_feature = self.hidden_norm(feature)
         residual = normed_feature if self.norm_before_residual else feature
         x = torch.cat((normed_embeds, normed_feature), dim=-1)
-        x = self.self_attn(x, positions)
+        x = self.self_attn(x, positions, attn_mask)
         x = residual + x
 
         residual = x
@@ -170,6 +245,15 @@ def __init__(self, config: Eagle3Config):
             persistent=False,
         )
         self.register_buffer("t2d", torch.zeros(1, dtype=torch.bool), persistent=False)
+        # cache_positions[i] = i; used to build the causal mask over the KV cache
+        # without introducing dynamic-shape index tensors at runtime.
+        self.register_buffer(
+            "cache_positions",
+            torch.arange(config.max_seq_len, dtype=torch.long),
+            persistent=False,
+        )
+        # Eager-only end of the valid contiguous cache prefix (see forward_cached).
+        self._cache_valid_end = 0
 
     def fuse(self, aux: torch.Tensor) -> torch.Tensor:
         """Fuse concatenated target aux hidden states (B,T,3*D) -> feature (B,T,D)."""
@@ -193,23 +277,112 @@ def forward(
         input_embeds: torch.Tensor,
         feature: torch.Tensor,
         positions: torch.Tensor,
+        attn_mask: torch.Tensor = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """Run the midlayer over a sequence.
 
+        ``attn_mask=None`` runs stateless over the full sequence; an explicit
+        mask uses the incremental KV cache (see ``forward_cached``).
+
         Returns (draft_logits, g):
           draft_logits: (B, T, draft_vocab_size) over the reduced vocab.
           g:            (B, T, hidden) midlayer output — the recurrent feature.
         """
-        g = self.midlayer(input_embeds, feature, positions)
+        g = self.midlayer(input_embeds, feature, positions, attn_mask)
         draft_logits = self.lm_head(self.norm(g))
         return draft_logits, g
 
+    def _build_causal_mask(self, positions: torch.Tensor) -> torch.Tensor:
+        """Boolean (1, 1, T, max_seq_len) causal mask (True = attend).
+
+        Query position p attends to cache slot j iff j <= p. This is correct
+        only under the contiguous-from-0 invariant of ``forward_cached``: a query
+        at p attends to slots 0..p, all of which must already hold this
+        sequence's K/V. Rejected speculative tokens sit at slots > p (the next
+        query's p shrinks on rollback) and are excluded by the causal bound, so
+        they need no extra masking. A non-contiguous seed (e.g. writing only
+        slot 10 after reset) would wrongly attend to the zeroed slots 0..9 — see
+        ``forward_cached``.
+        """
+        q_pos = positions.unsqueeze(1)  # (T, 1)
+        cache_pos = self.cache_positions.unsqueeze(0)  # (1, max_seq_len)
+        return (q_pos >= cache_pos).unsqueeze(0).unsqueeze(0)
+
+    def forward_cached(
+        self,
+        input_embeds: torch.Tensor,
+        feature: torch.Tensor,
+        positions: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """KV-cached forward for prefill (T>1) and single-step draft decode (T=1).
+
+        Writes K/V at ``positions`` and attends over the cache. Like the target's
+        KV cache, attention scores against the whole ``max_seq_len`` buffer under
+        a static causal mask (export-friendly, no data-dependent slicing); what
+        this avoids versus a full recompute is re-running the prefix's
+        projections and MLP, not the attention score width.
+
+        Invariant: writes must be contiguous from position 0. Seed a fresh
+        sequence (after ``reset_cache``) starting at position 0 and only ever
+        extend with the next contiguous positions; offset or gapped seeds attend
+        to unwritten (zeroed) slots and are not supported. Batch size must be 1.
+        """
+        if input_embeds.shape[0] != 1:
+            raise ValueError("forward_cached supports batch size 1 only")
+        if not torch.compiler.is_compiling():
+            self._validate_contiguous(positions)
+        return self.forward(
+            input_embeds, feature, positions, self._build_causal_mask(positions)
+        )
+
+    def _validate_contiguous(self, positions: torch.Tensor) -> None:
+        """Eager-only guard for the contiguous-from-0 cache invariant.
+
+        Tracks the end of the valid contiguous prefix (reset by ``reset_cache``).
+        A write may overwrite already-written slots (speculative rollback) but
+        must be contiguous and ascending and must not start beyond the valid
+        prefix, which would leave unwritten (zeroed) slots below it in the
+        attention window. A rollback overwrite truncates the valid prefix to the
+        end of the write, so a slot above it is treated as stale and a later
+        write that skips it is rejected until it is rewritten. Skipped under
+        export/compile, where positions are traced tensors and the runner owns
+        the contract.
+        """
+        start = int(positions[0])
+        length = int(positions.shape[0])
+        expected = torch.arange(start, start + length, device=positions.device)
+        if not torch.equal(positions, expected):
+            raise ValueError(
+                f"forward_cached positions must be contiguous ascending, "
+                f"got {positions.tolist()}"
+            )
+        if start > self._cache_valid_end:
+            raise ValueError(
+                f"non-contiguous cache seed: positions start at {start} but only "
+                f"{self._cache_valid_end} slot(s) are valid; seed from 0 after "
+                f"reset_cache"
+            )
+        # A write defines the valid prefix up to its end; slots above it (from an
+        # earlier longer write that this one rolled back) are now stale.
+        self._cache_valid_end = start + length
+
+    def reset_cache(self) -> None:
+        self.midlayer.self_attn.kv_cache.reset()
+        self._cache_valid_end = 0
+
+    def allocate_kv_cache(self, dtype: torch.dtype, device) -> None:
+        """(Re)allocate the KV cache in a given dtype/device (zeroed)."""
+        self.midlayer.self_attn.kv_cache.allocate(dtype, device)
+
     def draft_to_target(self, draft_ids: torch.Tensor) -> torch.Tensor:
         return draft_ids + self.d2t[draft_ids]
 
     @staticmethod
     def from_checkpoint(
-        model_dir: str, device: str = "cuda", dtype: torch.dtype = torch.bfloat16
+        model_dir: str,
+        device: str = "cuda",
+        dtype: torch.dtype = torch.bfloat16,
+        max_seq_len: int = 4096,
     ) -> tuple["Eagle3Draft", Eagle3Config]:
         import json
 
@@ -231,6 +404,7 @@ def from_checkpoint(
             aux_hidden_state_layers=cfg["eagle_aux_hidden_state_layer_ids"],
             norm_before_residual=cfg.get("norm_before_residual", False),
             norm_before_fc=cfg.get("norm_before_fc", False),
+            max_seq_len=max_seq_len,
         )
         if config.norm_before_fc:
             # This checkpoint variant requires an input RMSNorm before fc.
@@ -256,6 +430,12 @@ def from_checkpoint(
         model.register_buffer("d2t", state_dict.pop("d2t"), persistent=False)
         model.register_buffer("t2d", state_dict.pop("t2d"), persistent=False)
         model.load_state_dict(state_dict, strict=True, assign=True)
+        # Allocate the KV cache directly in the compute dtype on the target
+        # device *before* moving weights, so the float32 placeholder cache from
+        # __init__ is freed without ever being copied to the device. The
+        # subsequent .to(device) is a no-op for the (already-placed) cache and
+        # carries no dtype argument, so inv_freq stays float32.
+        model.allocate_kv_cache(dtype, device)
         model = model.to(device)
         assert (
             model.midlayer.self_attn.inv_freq.dtype == torch.float32
diff --git a/examples/models/eagle3/test_draft.py b/examples/models/eagle3/test_draft.py
index eb2587f5605..2534ca18da2 100644
--- a/examples/models/eagle3/test_draft.py
+++ b/examples/models/eagle3/test_draft.py
@@ -73,6 +73,119 @@ def test_norm_before_residual_changes_output():
     assert not torch.allclose(outs[0], outs[1]), "norm_before_residual had no effect"
 
 
+def test_kv_cache_matches_full_recompute():
+    # Cached prefill + single-step decode must equal stateless full recompute.
+    torch.manual_seed(0)
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    T, prefill = 6, 3
+    feat = model.fuse(
+        torch.randn(1, T, len(cfg.aux_hidden_state_layers) * cfg.target_hidden_size)
+    )
+    emb = model.embed(torch.randint(0, cfg.target_vocab_size, (T,))).unsqueeze(0)
+
+    with torch.no_grad():
+        ref_logits, ref_g = model(emb, feat, torch.arange(T))
+
+        model.reset_cache()
+        pl, pg = model.forward_cached(
+            emb[:, :prefill], feat[:, :prefill], torch.arange(prefill)
+        )
+        torch.testing.assert_close(pl, ref_logits[:, :prefill])
+        torch.testing.assert_close(pg, ref_g[:, :prefill])
+
+        for i in range(prefill, T):
+            sl, sg = model.forward_cached(
+                emb[:, i : i + 1], feat[:, i : i + 1], torch.arange(i, i + 1)
+            )
+            torch.testing.assert_close(sl, ref_logits[:, i : i + 1])
+            torch.testing.assert_close(sg, ref_g[:, i : i + 1])
+
+
+def test_reset_cache_isolates_sequences():
+    # A second sequence after reset_cache must match a fresh full recompute.
+    torch.manual_seed(0)
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    T = 4
+    feat = model.fuse(
+        torch.randn(1, T, len(cfg.aux_hidden_state_layers) * cfg.target_hidden_size)
+    )
+    emb = model.embed(torch.randint(0, cfg.target_vocab_size, (T,))).unsqueeze(0)
+    with torch.no_grad():
+        model.forward_cached(emb, feat, torch.arange(T))  # pollute the cache
+        model.reset_cache()
+        cached, _ = model.forward_cached(emb, feat, torch.arange(T))
+        ref, _ = model(emb, feat, torch.arange(T))
+    torch.testing.assert_close(cached, ref)
+
+
+def test_offset_seed_after_reset_is_rejected():
+    # The contiguous-from-0 invariant is enforced in eager: an offset seed would
+    # attend to zeroed slots, so forward_cached rejects it outright.
+    torch.manual_seed(0)
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    T = 4
+    feat = model.fuse(
+        torch.randn(1, T, len(cfg.aux_hidden_state_layers) * cfg.target_hidden_size)
+    )
+    emb = model.embed(torch.randint(0, cfg.target_vocab_size, (T,))).unsqueeze(0)
+    model.reset_cache()
+    with pytest.raises(ValueError, match="non-contiguous cache seed"):
+        model.forward_cached(emb, feat, torch.arange(10, 10 + T))
+
+
+def test_gapped_positions_rejected():
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    emb = torch.randn(1, 3, cfg.hidden_size)
+    feat = torch.randn(1, 3, cfg.hidden_size)
+    model.reset_cache()
+    with pytest.raises(ValueError, match="contiguous ascending"):
+        model.forward_cached(emb, feat, torch.tensor([0, 2, 3]))
+
+
+def test_rollback_reseed_is_allowed():
+    # Overwriting already-written slots (speculative rollback) must be accepted.
+    torch.manual_seed(0)
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    emb = model.embed(torch.randint(0, cfg.target_vocab_size, (6,))).unsqueeze(0)
+    feat = model.fuse(
+        torch.randn(1, 6, len(cfg.aux_hidden_state_layers) * cfg.target_hidden_size)
+    )
+    with torch.no_grad():
+        model.reset_cache()
+        model.forward_cached(emb, feat, torch.arange(6))  # write slots 0..5
+        # re-decode at slot 4 (a rejected proposal rolled back) — allowed.
+        model.forward_cached(emb[:, 4:5], feat[:, 4:5], torch.arange(4, 5))
+
+
+def test_post_rollback_gap_is_rejected():
+    # A rollback overwrite shrinks the valid prefix: after writing 0..5 then
+    # re-decoding slot 4, slot 5 holds stale (rejected) K/V, so a write starting
+    # at 6 must be rejected until slot 5 is rewritten.
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    model.reset_cache()
+    model._validate_contiguous(torch.arange(6))  # write slots 0..5
+    model._validate_contiguous(torch.arange(4, 5))  # rollback overwrite slot 4
+    with pytest.raises(ValueError, match="non-contiguous"):
+        model._validate_contiguous(torch.arange(6, 7))  # slot 5 stale -> rejected
+    model._validate_contiguous(torch.arange(5, 6))  # rewrite slot 5 -> ok
+    model._validate_contiguous(torch.arange(6, 7))  # now slot 6 -> ok
+
+
+def test_forward_cached_rejects_batch_gt_1():
+    cfg = tiny_config()
+    model = Eagle3Draft(cfg).to(torch.float32).eval()
+    emb = torch.randn(2, 3, cfg.hidden_size)
+    feat = torch.randn(2, 3, cfg.hidden_size)
+    with pytest.raises(ValueError, match="batch size 1"):
+        model.forward_cached(emb, feat, torch.arange(3))
+
+
 def test_draft_to_target_mapping():
     model = Eagle3Draft(tiny_config()).eval()
     model.d2t.copy_(torch.arange(model.config.draft_vocab_size))  # offset = id