diff --git a/crates/xserv-server/src/engine.rs b/crates/xserv-server/src/engine.rs
index 98ace2a..e280052 100644
--- a/crates/xserv-server/src/engine.rs
+++ b/crates/xserv-server/src/engine.rs
@@ -260,23 +260,41 @@ impl Engine {
                     &tokens, &positions, &slots, &mut self.paged_cache,
                 );
 
-                // Sample per-sequence from batched logits [B, vocab_size]
-                let vocab_size = logits.shape()[1];
-                let logits_cpu = logits.to_device(xserv_tensor::Device::Cpu);
-                let data = logits_cpu.as_slice::<half::bf16>();
-                for (j, &i) in decode_indices.iter().enumerate() {
-                    let row_start = j * vocab_size;
-                    let row_logits = &data[row_start..row_start + vocab_size];
-                    let next = if running[i].sampling.temperature == 0.0 {
-                        row_logits.iter().enumerate()
-                            .max_by(|a, b| a.1.to_f32().partial_cmp(&b.1.to_f32()).unwrap())
-                            .map(|(idx, _)| idx as u32).unwrap()
-                    } else {
-                        let row_tensor = xserv_tensor::Tensor::from_slice(row_logits, &[1, vocab_size]);
-                        sample(&row_tensor, &running[i].sampling)
-                    };
-                    running[i].generated_tokens.push(next);
-                    emit_token(&self.tokenizer, &mut running[i], next);
+                // Fast path: every active sequence is greedy → run argmax on
+                // the GPU and only D2H the chosen token ids (a few bytes per
+                // sequence) instead of the full [B, vocab_size] BF16 logits
+                // (~1.2 MB for B=4, Qwen3 vocab=152K).
+                let all_greedy = decode_indices.iter()
+                    .all(|&i| running[i].sampling.temperature == 0.0);
+                if all_greedy {
+                    let next_ids = xserv_kernels::argmax_bf16_to_host(&logits);
+                    for (j, &i) in decode_indices.iter().enumerate() {
+                        let next = next_ids[j];
+                        running[i].generated_tokens.push(next);
+                        emit_token(&self.tokenizer, &mut running[i], next);
+                    }
+                } else {
+                    // Mixed sampling: keep the CPU path for now (top-k/top-p
+                    // sampling still runs there). Only the rows that need it
+                    // get exercised; greedy rows could in principle reuse the
+                    // GPU argmax but the CPU pass is short for B<=4.
+                    let vocab_size = logits.shape()[1];
+                    let logits_cpu = logits.to_device(xserv_tensor::Device::Cpu);
+                    let data = logits_cpu.as_slice::<half::bf16>();
+                    for (j, &i) in decode_indices.iter().enumerate() {
+                        let row_start = j * vocab_size;
+                        let row_logits = &data[row_start..row_start + vocab_size];
+                        let next = if running[i].sampling.temperature == 0.0 {
+                            row_logits.iter().enumerate()
+                                .max_by(|a, b| a.1.to_f32().partial_cmp(&b.1.to_f32()).unwrap())
+                                .map(|(idx, _)| idx as u32).unwrap()
+                        } else {
+                            let row_tensor = xserv_tensor::Tensor::from_slice(row_logits, &[1, vocab_size]);
+                            sample(&row_tensor, &running[i].sampling)
+                        };
+                        running[i].generated_tokens.push(next);
+                        emit_token(&self.tokenizer, &mut running[i], next);
+                    }
                 }
             }