diff --git a/docs/design/flashnext_16gb_host_kv.md b/docs/design/flashnext_16gb_host_kv.md
index 0f65481f5..4387786ca 100644
--- a/docs/design/flashnext_16gb_host_kv.md
+++ b/docs/design/flashnext_16gb_host_kv.md
@@ -321,3 +321,10 @@ Ordinary dense segment scratch is bounded by the admitted K2560/N4096
 shapes, and shared scratch by K2560/N320 or K160/N2560. Code, coefficient
 and partial storage decreases by 34.5 MiB per device without changing their
 layouts, arithmetic or supported shapes.
+
+The subsequent M5 graph capture reached the fused side projection and failed
+because its FP32 split-K workspace inherited the model loader's FP16 default
+dtype. Allocate that workspace explicitly as FP32. Six formats passed both
+FP16 and FP32 default-dtype tests, shared-bank checks and changed-input M5/M20
+graphs (12 tests). This repairs workspace typing; it does not change weight
+or activation precision. Full-model serving remains pending.
diff --git a/tests/kernels/quantization/test_sm70_dmv13_side.py b/tests/kernels/quantization/test_sm70_dmv13_side.py
index 653d2392c..3b3d5e083 100644
--- a/tests/kernels/quantization/test_sm70_dmv13_side.py
+++ b/tests/kernels/quantization/test_sm70_dmv13_side.py
@@ -16,6 +16,16 @@ pytestmark = pytest.mark.skipif(
 BLOCK = {8: 34, 12: 144, 13: 176, 14: 210, 20: 18, 23: 136}
 
 
+@pytest.fixture(params=[torch.float32, torch.float16])
+def default_dtype(request):
+    previous = torch.get_default_dtype()
+    torch.set_default_dtype(request.param)
+    try:
+        yield
+    finally:
+        torch.set_default_dtype(previous)
+
+
 def _raw(rng, rows, k, qtype):
     size = BLOCK[qtype]
     block = 32 if qtype in (8, 20) else 256
@@ -84,7 +94,7 @@ def test_side_projection_matches_dequant(qtype, tokens, merged_bf16):
 
 
 @pytest.mark.parametrize("qtype", [8, 12, 13, 14, 20, 23])
-def test_shared_segment_planes_preserve_side_and_m20_graph(qtype):
+def test_shared_segment_planes_preserve_side_and_m20_graph(qtype, default_dtype):
     from types import SimpleNamespace
 
     from vllm.model_executor.layers.quantization.gguf_dense_hmma_formats import (
@@ -113,6 +123,7 @@ def test_shared_segment_planes_preserve_side_and_m20_graph(qtype):
     extra = (torch.randn(24, k, device="cuda") * 0.02).half()
     proj = Dmv13Projection(_Shard(raw, qtype), _Extra(extra))
     assert proj.ready
+    assert proj.workspace.dtype == torch.float32
     layer.sm70_side_projection = proj
     _register_banks(layer, "sm70_side_projection", proj)
     x = torch.randn(5, k, device="cuda", dtype=torch.float16)
diff --git a/vllm/model_executor/layers/quantization/sm70_dmv13_projection.py b/vllm/model_executor/layers/quantization/sm70_dmv13_projection.py
index 40cfcdc7e..cb6037f48 100644
--- a/vllm/model_executor/layers/quantization/sm70_dmv13_projection.py
+++ b/vllm/model_executor/layers/quantization/sm70_dmv13_projection.py
@@ -121,7 +121,7 @@ class Dmv13Projection:
                 return
             self.extra = weight.contiguous()
         tiles = sum((w + 31) // 32 for w in self.widths)
-        self.workspace = torch.zeros(tiles * 256, device=device)
+        self.workspace = torch.zeros(tiles * 256, device=device, dtype=torch.float32)
         self.counters = torch.zeros(tiles, device=device, dtype=torch.int32)
         self.ready = hasattr(torch.ops._C, "sm70_dmv13_out")