diff --git a/dflash/src/internal.h b/dflash/src/internal.h
index 20617955d..3c66af925 100644
--- a/dflash/src/internal.h
+++ b/dflash/src/internal.h
@@ -156,6 +156,7 @@ struct DraftLayer {
     ggml_tensor * w_gate;
     ggml_tensor * w_up;
     ggml_tensor * w_down;
+    bool is_swa = false;  // sliding window attention (Qwen3.6 draft)
 };
 
 struct DraftWeights {
@@ -175,6 +176,7 @@ struct DraftWeights {
     int head_dim  = DFLASH27B_TARGET_HEAD_DIM;         // 128
     int n_embd    = DFLASH27B_TARGET_HIDDEN;           // 5120
     int n_ff      = DFLASH27B_TARGET_INTERMEDIATE;     // 17408
+    int swa_window = 0;  // sliding window size (0 = full attention, 2048 for Qwen3.6 draft)
 };
 
 bool load_draft_safetensors(const std::string & path,
diff --git a/dflash/src/qwen3_dflash_graph.cpp b/dflash/src/qwen3_dflash_graph.cpp
index 638bd8735..9b82d317e 100644
--- a/dflash/src/qwen3_dflash_graph.cpp
+++ b/dflash/src/qwen3_dflash_graph.cpp
@@ -118,8 +118,27 @@ DraftGraphOutputs build_draft_graph(
         V = ggml_cont   (ctx, V);
 
         // ── 2f. Non-causal flash attention; GQA broadcast handled internally.
+        //   For SWA layers (Qwen3.6 draft): apply sliding window mask
+        //   limiting context K/V to the last `swa_window` positions.
         const float scale = 1.0f / std::sqrt((float)head_dim);
-        ggml_tensor * attn = ggml_flash_attn_ext(ctx, Q, K, V, /*mask=*/nullptr,
+        ggml_tensor * attn_mask = nullptr;
+        if (L.is_swa && w.swa_window > 0 && total_k > w.swa_window) {
+            // Build a mask that blocks attention beyond the window.
+            // mask shape: [total_k, q_len] — element (k, q) = 0 (attend) or -inf (block)
+            // For SWA: each query at position p attends to K positions in [p - window, p + window]
+            // But in DFlash non-causal mode, queries are at positions [ctx_len..ctx_len+q_len-1]
+            // and keys span [0..total_k-1]. SWA means keys older than window are masked.
+            const int win = w.swa_window;
+            attn_mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, total_k, q_len);
+            ggml_set_name(attn_mask, "swa_mask");
+            ggml_set_input(attn_mask);
+            // NOTE: mask data will be set at graph compute time by the caller.
+            // For now, we pass nullptr and let full attention run — the mask
+            // setup requires knowing absolute positions which are in `in.positions_k`.
+            // TODO: implement mask fill in the caller or use ggml_diag_mask_inf
+            attn_mask = nullptr; // fallback to full attention until mask fill is wired
+        }
+        ggml_tensor * attn = ggml_flash_attn_ext(ctx, Q, K, V, attn_mask,
                                                  scale, /*max_bias=*/0.0f,
                                                  /*logit_softcap=*/0.0f);
         // attn result: [n_embd_v=head_dim, n_head, n_batch=q_len, 1]
diff --git a/dflash/src/safetensors_draft.cpp b/dflash/src/safetensors_draft.cpp
index c499025ff..3e669ffe0 100644
--- a/dflash/src/safetensors_draft.cpp
+++ b/dflash/src/safetensors_draft.cpp
@@ -433,6 +433,68 @@ bool load_draft_safetensors(const std::string & path,
         }
     }
 
+    // ── 4b. Read config.json for SWA layer_types (Qwen3.6 draft) ──
+    {
+        // config.json sits next to model.safetensors
+        std::string dir;
+        auto slash = path.find_last_of('/');
+        if (slash != std::string::npos) {
+            dir = path.substr(0, slash);
+        } else {
+            dir = ".";  // bare filename — look in CWD
+        }
+        std::string cfg_path = dir + "/config.json";
+        FILE * f = std::fopen(cfg_path.c_str(), "r");
+        if (f) {
+            std::fseek(f, 0, SEEK_END);
+            long flen = std::ftell(f);
+            std::fseek(f, 0, SEEK_SET);
+            std::string cfg(flen, '\0');
+            std::fread(&cfg[0], 1, flen, f);
+            std::fclose(f);
+
+            // Parse sliding_window
+            auto sw_pos = cfg.find("\"sliding_window\"");
+            if (sw_pos != std::string::npos) {
+                auto colon = cfg.find(':', sw_pos);
+                if (colon != std::string::npos) {
+                    int sw = std::atoi(cfg.c_str() + colon + 1);
+                    if (sw > 0) out.swa_window = sw;
+                }
+            }
+
+            // Parse layer_types array
+            auto lt_pos = cfg.find("\"layer_types\"");
+            if (lt_pos != std::string::npos) {
+                auto arr_start = cfg.find('[', lt_pos);
+                auto arr_end   = cfg.find(']', arr_start);
+                if (arr_start != std::string::npos && arr_end != std::string::npos) {
+                    std::string arr = cfg.substr(arr_start, arr_end - arr_start + 1);
+                    int li = 0;
+                    size_t search_pos = 0;
+                    while (li < n_layers && search_pos < arr.size()) {
+                        auto q1 = arr.find('"', search_pos);
+                        if (q1 == std::string::npos) break;
+                        auto q2 = arr.find('"', q1 + 1);
+                        if (q2 == std::string::npos) break;
+                        std::string lt = arr.substr(q1 + 1, q2 - q1 - 1);
+                        out.layers[li].is_swa = (lt == "sliding_attention");
+                        li++;
+                        search_pos = q2 + 1;
+                    }
+                }
+            }
+
+            int n_swa = 0;
+            for (int il = 0; il < n_layers; il++) {
+                if (out.layers[il].is_swa) n_swa++;
+            }
+            if (n_swa > 0) {
+                fprintf(stderr, "[draft] SWA layers: %d/%d (window=%d)\n", n_swa, n_layers, out.swa_window);
+            }
+        }
+    }
+
     // ── 5. Allocate backend buffer, copy bytes ───────────────────
     out.buf = ggml_backend_alloc_ctx_tensors(out.ctx, backend);
     if (!out.buf) { set_last_error("ggml_backend_alloc_ctx_tensors failed (draft)"); return false; }