diff --git a/ggml/src/ggml-sycl/fattn-onednn.cpp b/ggml/src/ggml-sycl/fattn-onednn.cpp
index f2e12ef1aeff..0cea85492431 100644
--- a/ggml/src/ggml-sycl/fattn-onednn.cpp
+++ b/ggml/src/ggml-sycl/fattn-onednn.cpp
@@ -245,16 +245,11 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso
     E.cp.execute(strm, ti, {to});
 
     permute_sdpa_out_sycl(outf.get(), (float *) dst->data, mb, H, q, d, stream);
-    // Single device: no sync is required, and actually PP perf is ~6% > wait_and_throw() (tested on llama-3.1-8b & qwen3.6-27b, both Q8_0, with Arc B70).
-    // Any future multi-GPU refactor MUST re-measure this single-device path and keep the best
-    // single-device PP speed. Otherwise (multiple devices/streams can race the reuse):
-    if (ggml_sycl_info().device_count > 1) {
-        // cont_to_f16 -> oneDNN execute -> permute is async on this stream, but the
-        // pool_alloc*s above free their device buffers at host return. Without this wait the next
-        // scheduler op re-acquires those bytes while the GPU is still computing the SDPA, turning
-        // it into garbage and collapsing multi-turn output to a single repeated token ("GGGGG...").
-        stream->wait_and_throw();
-    }
+    // cont_to_f16 -> oneDNN execute -> permute is async on this stream, but the
+    // pool_alloc*s above free their device buffers at host return. Without this wait the next
+    // scheduler op re-acquires those bytes while the GPU is still computing the SDPA, turning
+    // it into garbage and collapsing multi-turn output to a single repeated token ("GGGGG...").
+    stream->wait_and_throw();
 }
 catch (const std::exception & e) {
     // any oneDNN/SYCL failure is non-fatal: fall back to the existing kernel (strictly additive).