diff --git a/benchmark/scripts/benchmark_model_configs.py b/benchmark/scripts/benchmark_model_configs.py
index da6f0fa0b..0be4d6a00 100644
--- a/benchmark/scripts/benchmark_model_configs.py
+++ b/benchmark/scripts/benchmark_model_configs.py
@@ -291,6 +291,68 @@ def estimate_kernel_peak_memory(probe_fn: Callable[[], torch.Tensor]) -> int:
     return max(1, peak_bytes)
 
 
+def compute_seq_len_sweep_config_with_probe(
+    model_cfg: ModelConfig,
+    probe_fn: Callable[[], torch.Tensor],
+    probe_seq_len: int,
+    probe_batch_size: int = 1,
+    scaling_method: Literal["linear", "quadratic"] = "linear",
+    memory_utilization: float = 0.4,
+    max_seq_len: Optional[int] = None,
+    max_batch_size: int = 32,
+) -> SeqLenSweepConfig:
+    """Compute safe sweep config from a probe, supporting non-linear seq-len scaling.
+
+    Wraps :func:`estimate_kernel_peak_memory` and inverts the memory model
+    according to *scaling_method*. Linear-scaling kernels can keep using
+    :func:`compute_seq_len_sweep_config` directly; this helper exists for
+    kernels whose memory grows non-linearly with seq_len (e.g. attention
+    kernels with O(L^2) scratch).
+
+    Args:
+        model_cfg: Model architecture config.
+        probe_fn: Callable that performs setup, runs a forward pass, and
+            returns an output tensor suitable for ``.backward()``. Same
+            contract as :func:`estimate_kernel_peak_memory`'s *probe_fn*.
+        probe_seq_len: Sequence length used inside *probe_fn*. Required so
+            the inversion can isolate the seq-len-dependent term.
+        probe_batch_size: Batch size used inside *probe_fn*. Defaults to 1.
+        scaling_method: How peak memory scales with seq_len, holding batch
+            size fixed.
+
+            - ``"linear"``: peak ~ B * L. Inversion: ``L_max = usable / (B * c_per_BL)``.
+            - ``"quadratic"``: peak ~ B * L^2. Inversion: ``L_max = sqrt(usable / (B * c_per_BL2))``.
+        memory_utilization: Fraction of total device memory to target (0 to 1).
+        max_seq_len: Hard upper bound for sequence length. Defaults to
+            ``model_cfg.max_position_embeddings``.
+        max_batch_size: Hard upper bound for batch size.
+    """
+    if scaling_method not in ("linear", "quadratic"):
+        raise ValueError(f"scaling_method must be 'linear' or 'quadratic', got {scaling_method!r}")
+
+    peak_bytes = estimate_kernel_peak_memory(probe_fn=probe_fn)
+
+    total_memory_gb = get_total_gpu_memory()
+    usable_bytes = total_memory_gb * (1024**3) * memory_utilization
+
+    if max_seq_len is None:
+        max_seq_len = model_cfg.max_position_embeddings
+
+    batch_size = max(1, min(max_batch_size, probe_batch_size))
+
+    if scaling_method == "linear":
+        c_per_BL = max(1.0, peak_bytes / (probe_batch_size * probe_seq_len))
+        max_seq_len_from_mem = max(1, int(usable_bytes / (batch_size * c_per_BL)))
+    else:
+        c_per_BL2 = max(1.0, peak_bytes / (probe_batch_size * probe_seq_len * probe_seq_len))
+        max_seq_len_from_mem = max(1, int(math.sqrt(usable_bytes / (batch_size * c_per_BL2))))
+
+    seq_len = min(max_seq_len, max_seq_len_from_mem)
+    seq_len = 2 ** int(math.log2(seq_len)) if seq_len >= 1024 else 1024
+
+    return SeqLenSweepConfig(batch_size=batch_size, seq_len=seq_len)
+
+
 def compute_seq_len_sweep_config(
     model_cfg: ModelConfig,
     kernel_bytes_per_token: Optional[int] = None,
diff --git a/benchmark/scripts/benchmark_sparse_multi_token_attention.py b/benchmark/scripts/benchmark_sparse_multi_token_attention.py
index e35fdbba3..646c229f0 100644
--- a/benchmark/scripts/benchmark_sparse_multi_token_attention.py
+++ b/benchmark/scripts/benchmark_sparse_multi_token_attention.py
@@ -7,8 +7,7 @@
 
 from benchmark_model_configs import MODEL_REGISTRY
 from benchmark_model_configs import compute_model_config_sweep_config
-from benchmark_model_configs import compute_seq_len_sweep_config
-from benchmark_model_configs import estimate_kernel_peak_memory
+from benchmark_model_configs import compute_seq_len_sweep_config_with_probe
 from benchmark_model_configs import get_benchmark_model_config
 from utils import QUANTILES
 from utils import SingleBenchmarkRunInput
@@ -329,10 +328,13 @@ def _probe():
             _, _, fwd_fn = _setup_sparse_multi_token_attention(probe_input)
             return fwd_fn()
 
-        peak_bytes = estimate_kernel_peak_memory(probe_fn=_probe)
-        # Memory scales as O(L^2), so compute bytes per L^2
-        kernel_bpt = peak_bytes // (probe_L * probe_L)
-        config = compute_seq_len_sweep_config(model, kernel_bytes_per_token=kernel_bpt)
+        config = compute_seq_len_sweep_config_with_probe(
+            model_cfg=model,
+            probe_fn=_probe,
+            probe_seq_len=probe_L,
+            probe_batch_size=B,
+            scaling_method="quadratic",
+        )
 
         common_configs = {
             "kernel_name": "sparse_multi_token_attention",