diff --git a/crates/xserv-model/src/qwen3.rs b/crates/xserv-model/src/qwen3.rs
index c012dcd..1900c64 100644
--- a/crates/xserv-model/src/qwen3.rs
+++ b/crates/xserv-model/src/qwen3.rs
@@ -20,6 +20,11 @@ pub struct Qwen3 {
     tp: Option<std::sync::Arc<xserv_distributed::TpContext>>,
     local_num_heads: usize,    // = num_heads / world
     local_num_kv_heads: usize, // = num_kv_heads / world
+    // Pipeline parallelism (Phase 18): this stage holds a contiguous slice of
+    // layers. `is_first_stage` owns `embed_tokens`; `is_last_stage` owns
+    // `norm`/`lm_head_t`. Both true for single-GPU / TP (the whole model).
+    is_first_stage: bool,
+    is_last_stage: bool,
 }
 
 struct Qwen3Block {
@@ -113,9 +118,267 @@ impl Qwen3 {
             lm_head_t,
             rope_cache,
             tp,
+            is_first_stage: true,
+            is_last_stage: true,
         }
     }
 
+    /// Pipeline-parallel load (Phase 18). This stage holds the contiguous layer
+    /// range `[stage*L, (stage+1)*L)` with `L = num_layers / num_stages`; only
+    /// stage 0 keeps `embed_tokens` and only the last stage keeps `norm`/`lm_head`
+    /// (others get a 1x1 placeholder, guarded by the stage flags and never used).
+    /// Heads are NOT split (PP is orthogonal to TP), so each stage runs full
+    /// attention/MLP over its layers and hands off the `[tokens, hidden]` hidden
+    /// state to the next stage (the engine does the NCCL send/recv).
+    pub fn from_weights_pp(
+        config: ModelConfig,
+        mut w: HashMap<String, Tensor>,
+        stage: usize,
+        num_stages: usize,
+        device: u32,
+    ) -> Self {
+        crate::init_kernels();
+        let dev = Device::Cuda(device);
+        assert!(num_stages >= 1);
+        let num_layers = config.num_layers();
+        assert!(num_layers % num_stages == 0, "num_layers {num_layers} not divisible by pp {num_stages}");
+        let per_stage = num_layers / num_stages;
+        let lo = stage * per_stage;
+        let hi = lo + per_stage;
+        let is_first_stage = stage == 0;
+        let is_last_stage = stage == num_stages - 1;
+
+        let take = |w: &mut HashMap<String, Tensor>, name: &str| -> Tensor {
+            w.remove(name).unwrap_or_else(|| panic!("missing weight: {name}"))
+        };
+        let repl = |t: Tensor| -> Tensor { t.to_device(dev) };
+        // Pre-transpose like the TP path's `col`/`row` do for world==1 (no shard).
+        let wt = |t: Tensor| -> Tensor { t.to_device(dev).transpose(0, 1).contiguous() };
+        let placeholder = || Tensor::from_slice(&[bf16::ZERO], &[1, 1]).to_device(dev);
+
+        let embed_tokens = if is_first_stage { repl(take(&mut w, "model.embed_tokens.weight")) } else { placeholder() };
+        let norm = if is_last_stage { repl(take(&mut w, "model.norm.weight")) } else { placeholder() };
+        let lm_head_t = if is_last_stage { wt(take(&mut w, "lm_head.weight")) } else { placeholder() };
+
+        let rope_cache = RopeCache::new(
+            config.max_seq_len(),
+            config.head_dim(),
+            config.rope_theta.unwrap_or(1_000_000.0) as f32,
+        );
+
+        let mut layers = Vec::with_capacity(per_stage);
+        eprintln!(
+            "[pp] stage {stage}/{num_stages}: layers [{lo}, {hi}) {}{}",
+            if is_first_stage { "+embed " } else { "" },
+            if is_last_stage { "+norm+lm_head" } else { "" }
+        );
+        for i in lo..hi {
+            let p = format!("model.layers.{i}");
+            layers.push(Qwen3Block {
+                input_norm: repl(take(&mut w, &format!("{p}.input_layernorm.weight"))),
+                q_proj_wt: wt(take(&mut w, &format!("{p}.self_attn.q_proj.weight"))),
+                k_proj_wt: wt(take(&mut w, &format!("{p}.self_attn.k_proj.weight"))),
+                v_proj_wt: wt(take(&mut w, &format!("{p}.self_attn.v_proj.weight"))),
+                o_proj_wt: wt(take(&mut w, &format!("{p}.self_attn.o_proj.weight"))),
+                q_norm: repl(take(&mut w, &format!("{p}.self_attn.q_norm.weight"))),
+                k_norm: repl(take(&mut w, &format!("{p}.self_attn.k_norm.weight"))),
+                post_norm: repl(take(&mut w, &format!("{p}.post_attention_layernorm.weight"))),
+                gate_proj_wt: wt(take(&mut w, &format!("{p}.mlp.gate_proj.weight"))),
+                up_proj_wt: wt(take(&mut w, &format!("{p}.mlp.up_proj.weight"))),
+                down_proj_wt: wt(take(&mut w, &format!("{p}.mlp.down_proj.weight"))),
+            });
+        }
+
+        Self {
+            local_num_heads: config.num_heads(),
+            local_num_kv_heads: config.num_kv_heads(),
+            config,
+            embed_tokens,
+            layers,
+            norm,
+            lm_head_t,
+            rope_cache,
+            tp: None,
+            is_first_stage,
+            is_last_stage,
+        }
+    }
+
+    /// Stage-0 token embedding: `[S]` token ids -> `[S, hidden]` hidden state.
+    pub fn embed(&self, token_ids: &[u32]) -> Tensor {
+        debug_assert!(self.is_first_stage);
+        embedding(&self.embed_tokens, token_ids)
+    }
+
+    /// Last-stage head: `[*, hidden]` -> logits `[*, vocab]`.
+    pub fn head(&self, x: &Tensor) -> Tensor {
+        debug_assert!(self.is_last_stage);
+        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;
+        let x = rmsnorm(x, &self.norm, eps);
+        matmul_2d(&x, &self.lm_head_t)
+    }
+
+    pub fn pp_is_first(&self) -> bool { self.is_first_stage }
+    pub fn pp_is_last(&self) -> bool { self.is_last_stage }
+
+    /// PP prefill over THIS stage's layers. `x` is `[S, hidden]` (stage 0: from
+    /// `embed`; otherwise received from the previous stage). Writes K/V for this
+    /// stage's layers into `paged_cache` (indexed by local layer id) and returns
+    /// the `[S, hidden]` hidden state to hand to the next stage. Same kernels as
+    /// `forward_prefill_paged`, minus embedding and the final norm/lm_head.
+    pub fn forward_layers_prefill(
+        &self,
+        mut x: Tensor,
+        slot: usize,
+        paged_cache: &mut PagedKVCache,
+    ) -> Tensor {
+        let new_tokens = x.shape()[0];
+        let pos_offset = paged_cache.seq_len(slot);
+        let num_heads = self.local_num_heads;
+        let num_kv_heads = self.local_num_kv_heads;
+        let head_dim = self.config.head_dim();
+        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;
+
+        paged_cache.ensure_capacity(slot, pos_offset + new_tokens);
+        paged_cache.advance_seq_len(slot, new_tokens);
+
+        let positions: Vec<u32> = (pos_offset..pos_offset + new_tokens).map(|p| p as u32).collect();
+
+        for (layer_idx, layer) in self.layers.iter().enumerate() {
+            let residual = x.clone();
+            let normed = rmsnorm(&x, &layer.input_norm, eps);
+
+            let q = matmul_2d(&normed, &layer.q_proj_wt);
+            let k = matmul_2d(&normed, &layer.k_proj_wt);
+            let v = matmul_2d(&normed, &layer.v_proj_wt);
+
+            let q = xserv_kernels::reshape_heads_gpu(&q, new_tokens, num_heads, head_dim);
+            let k = xserv_kernels::reshape_heads_gpu(&k, new_tokens, num_kv_heads, head_dim);
+            let v = xserv_kernels::reshape_heads_gpu(&v, new_tokens, num_kv_heads, head_dim);
+
+            let q = head_rmsnorm(&q, &layer.q_norm, eps);
+            let k = head_rmsnorm(&k, &layer.k_norm, eps);
+
+            let q = xserv_kernels::transpose_for_rope_gpu(&q, new_tokens, num_heads, head_dim);
+            let k = xserv_kernels::transpose_for_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);
+            rope_inplace(&q, &self.rope_cache, &positions);
+            rope_inplace(&k, &self.rope_cache, &positions);
+            let q = xserv_kernels::transpose_from_rope_gpu(&q, new_tokens, num_heads, head_dim);
+            let k = xserv_kernels::transpose_from_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);
+
+            paged_cache.append_tokens(slot, layer_idx, &k, &v, new_tokens, pos_offset);
+            let (k_full, v_full) = paged_cache.gather_kv_contiguous(slot, layer_idx);
+            let attn_out = flash_attention(&q, &k_full, &v_full, true);
+
+            let attn_merged = xserv_kernels::merge_heads_gpu(&attn_out, new_tokens, num_heads, head_dim);
+            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);
+
+            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
+            let residual = x_new.clone();
+
+            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
+            let up = matmul_2d(&normed, &layer.up_proj_wt);
+            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
+            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
+            x = add_any(&residual, &down);
+        }
+        x
+    }
+
+    /// PP decode over THIS stage's layers. `x` is `[B, hidden]`. Returns
+    /// `[B, hidden]`. Positions are read from `paged_cache` (all stages advance
+    /// in lockstep, so they agree). Same kernels as `forward_decode_paged`.
+    pub fn forward_layers_decode(
+        &self,
+        mut x: Tensor,
+        seq_slots: &[usize],
+        paged_cache: &mut PagedKVCache,
+    ) -> Tensor {
+        let batch = seq_slots.len();
+        assert_eq!(x.shape()[0], batch);
+        let num_heads = self.local_num_heads;
+        let num_kv_heads = self.local_num_kv_heads;
+        let head_dim = self.config.head_dim();
+        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;
+
+        let positions: Vec<usize> = seq_slots.iter().map(|&s| paged_cache.seq_len(s)).collect();
+        let kv_lens: Vec<i32> = positions.iter().map(|&p| (p + 1) as i32).collect();
+        for (b, &slot) in seq_slots.iter().enumerate() {
+            paged_cache.ensure_capacity(slot, positions[b] + 1);
+        }
+        paged_cache.sync_active_batch_with_lens(seq_slots, &kv_lens);
+
+        let bt_ptr = paged_cache.block_table_gpu().as_ptr() as *const i32;
+        let cl_ptr = paged_cache.context_lens_gpu().as_ptr() as *const i32;
+        let max_blocks = paged_cache.max_blocks_per_seq();
+
+        for (layer_idx, layer) in self.layers.iter().enumerate() {
+            let residual = x.clone();
+            let normed = rmsnorm(&x, &layer.input_norm, eps);
+
+            let q_all = matmul_2d(&normed, &layer.q_proj_wt);
+            let k_all = matmul_2d(&normed, &layer.k_proj_wt);
+            let v_all = matmul_2d(&normed, &layer.v_proj_wt);
+
+            let mut q_rows: Vec<Tensor> = Vec::with_capacity(batch);
+            for b in 0..batch {
+                let q_row = row_view(&q_all, b);
+                let k_row = row_view(&k_all, b);
+                let v_row = row_view(&v_all, b);
+
+                let q = xserv_kernels::reshape_heads_gpu(&q_row, 1, num_heads, head_dim);
+                let k = xserv_kernels::reshape_heads_gpu(&k_row, 1, num_kv_heads, head_dim);
+                let v = xserv_kernels::reshape_heads_gpu(&v_row, 1, num_kv_heads, head_dim);
+
+                let q = head_rmsnorm(&q, &layer.q_norm, eps);
+                let k = head_rmsnorm(&k, &layer.k_norm, eps);
+
+                let q = xserv_kernels::transpose_for_rope_gpu(&q, 1, num_heads, head_dim);
+                let k = xserv_kernels::transpose_for_rope_gpu(&k, 1, num_kv_heads, head_dim);
+
+                let pos = [positions[b] as u32];
+                rope_inplace(&q, &self.rope_cache, &pos);
+                rope_inplace(&k, &self.rope_cache, &pos);
+
+                let q = xserv_kernels::transpose_from_rope_gpu(&q, 1, num_heads, head_dim);
+                let k = xserv_kernels::transpose_from_rope_gpu(&k, 1, num_kv_heads, head_dim);
+
+                paged_cache.append_tokens(seq_slots[b], layer_idx, &k, &v, 1, positions[b]);
+
+                let q_flat = xserv_kernels::merge_heads_gpu(&q, 1, num_heads, head_dim);
+                q_rows.push(q_flat);
+            }
+
+            let q_batched_2d = concat_rows(&q_rows);
+            let q_4d = q_batched_2d.reshape(&[batch, num_heads, 1, head_dim]);
+
+            let k_pool_ptr = paged_cache.k_pool(layer_idx).as_ptr() as *const std::ffi::c_void;
+            let v_pool_ptr = paged_cache.v_pool(layer_idx).as_ptr() as *const std::ffi::c_void;
+
+            let attn_out = xserv_kernels::paged_decode_attention(
+                &q_4d, k_pool_ptr, v_pool_ptr, bt_ptr, cl_ptr,
+                batch, num_heads, num_kv_heads, head_dim, max_blocks,
+            );
+
+            let attn_merged = attn_out.reshape(&[batch, num_heads * head_dim]);
+            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);
+
+            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
+            let residual = x_new.clone();
+
+            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
+            let up = matmul_2d(&normed, &layer.up_proj_wt);
+            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
+            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
+            x = add_any(&residual, &down);
+        }
+
+        for &slot in seq_slots {
+            paged_cache.advance_seq_len(slot, 1);
+        }
+        x
+    }
+
     /// In-place AllReduce(sum) of a partial `[*, hidden]` BF16 activation across
     /// TP ranks (no-op when not tensor-parallel). Used after o_proj and down_proj.
     #[inline]
diff --git a/crates/xserv-model/src/sampling.rs b/crates/xserv-model/src/sampling.rs
index 558f0cf..97bd01a 100644
--- a/crates/xserv-model/src/sampling.rs
+++ b/crates/xserv-model/src/sampling.rs
@@ -2,6 +2,7 @@ use half::bf16;
 use rand::Rng;
 use xserv_tensor::{DType, Device, Tensor};
 
+#[derive(Clone)]
 pub struct SamplingParams {
     pub temperature: f32,
     pub top_k: usize,