diff --git a/crates/apr-cli/src/commands/distill.rs b/crates/apr-cli/src/commands/distill.rs
index 8d73c02b2..b2240fcfb 100644
--- a/crates/apr-cli/src/commands/distill.rs
+++ b/crates/apr-cli/src/commands/distill.rs
@@ -606,6 +606,35 @@ pub(crate) fn run(
 /// The dispatch script (`scripts/dispatch-distill-phase-3-gx10.sh`)
 /// scales by step count rather than batch parallelism. Phase 2e
 /// generalizes via a fused-step trait method.
+/// PMAT-698e: cap max_position_embeddings before passing into the cuda
+/// trainer. See call sites in run_cuda_backend for the full rationale —
+/// CudaTransformerTrainer::for_inference uses this value as max_seq_len
+/// for ALL per-block scratch buffers, and the attention scores tensor
+/// is sized at num_heads * max_seq_len² * 4 bytes, which overflows
+/// the GPU memory budget for max_position_embeddings >= ~8192 even on
+/// the 128GB GB10 unified pool.
+///
+/// Default cap: 2048 (gives ~5.6 GB workspace for Qwen2.5-Coder-0.5B).
+/// Override via APR_DISTILL_MAX_SEQ_LEN env var (e.g. set to 4096 for
+/// longer-context distillation runs that still fit).
+#[cfg(all(feature = "training", feature = "cuda"))]
+fn cap_max_seq_len(native: Option<usize>) -> Option<usize> {
+    const DEFAULT_CAP: usize = 2048;
+    let cap = std::env::var("APR_DISTILL_MAX_SEQ_LEN")
+        .ok()
+        .and_then(|v| v.parse::<usize>().ok())
+        .unwrap_or(DEFAULT_CAP);
+    let n = native?;
+    let chosen = n.min(cap);
+    if chosen < n {
+        eprintln!(
+            "[PMAT-698e] capping max_position_embeddings {n} → {chosen} \
+             (override via APR_DISTILL_MAX_SEQ_LEN)"
+        );
+    }
+    Some(chosen)
+}
+
 #[cfg(all(feature = "training", feature = "cuda"))]
 #[allow(clippy::too_many_arguments)]
 fn run_cuda_backend(
@@ -672,7 +701,20 @@ fn run_cuda_backend(
         teacher_meta.intermediate_size,
         teacher_meta.num_layers,
         teacher_meta.vocab_size,
-        teacher_meta.max_position_embeddings,
+        // PMAT-698e: cap max_position_embeddings before passing into
+        // CudaTransformerTrainer::for_inference. The trainer uses this
+        // value verbatim as max_seq_len for ALL per-block scratch buffers,
+        // including the attention scores tensor sized at
+        //   num_heads * max_seq_len² * 4 bytes
+        // For Qwen2.5-Coder-0.5B (native max_position_embeddings=32768, 14
+        // heads), that's 14 * 32768² * 4 = 60 GB PER BLOCK; on a 24-layer
+        // model the total scratch footprint is ~1.4 TB, which overflows
+        // even GB10's 128 GB unified pool and surfaces as
+        // CUDA_ERROR_OUT_OF_MEMORY at "Block 0 upload". Distillation
+        // training rarely sees sequences longer than 2048 tokens; capping
+        // here gives ~5.6 GB workspace for the smoke. Caller can override
+        // via APR_DISTILL_MAX_SEQ_LEN env var.
+        cap_max_seq_len(teacher_meta.max_position_embeddings),
         teacher_meta.rms_norm_eps,
         teacher_meta.rope_theta,
         teacher_meta.architecture.as_deref(),
@@ -705,7 +747,8 @@ fn run_cuda_backend(
         student_meta.intermediate_size,
         student_meta.num_layers,
         student_meta.vocab_size,
-        student_meta.max_position_embeddings,
+        // PMAT-698e (see teacher comment above): same cap rationale.
+        cap_max_seq_len(student_meta.max_position_embeddings),
         student_meta.rms_norm_eps,
         student_meta.rope_theta,
         student_meta.architecture.as_deref(),