diff --git a/services/hwfit/hardware.py b/services/hwfit/hardware.py
index b6e7980..0af62a0 100644
--- a/services/hwfit/hardware.py
+++ b/services/hwfit/hardware.py
@@ -105,6 +105,8 @@ def _detect_nvidia():
         return None
 
     gpus = []
+    # Devices nvidia-smi lists with a real name but a non-numeric memory.total.
+    unified = []
     # nvidia-smi lists GPUs in index order (0,1,2,...), so the row position is
     # the CUDA device index we'd pass to CUDA_VISIBLE_DEVICES.
     for idx, line in enumerate(out.strip().split("\n")):
@@ -114,9 +116,32 @@ def _detect_nvidia():
                 vram_mb = float(parts[0])
                 gpus.append({"index": idx, "name": parts[1], "vram_gb": vram_mb / 1024.0})
             except ValueError:
+                # Grace Blackwell GB10 / DGX Spark and other unified-memory
+                # NVIDIA parts report memory.total as "[N/A]"/"Not Supported"
+                # because the GPU shares the system LPDDR pool instead of
+                # carrying discrete VRAM. Don't drop the device — remember it so
+                # we report a unified-memory GPU below rather than "No GPU" (#1340).
+                if parts[1]:
+                    unified.append({"index": idx, "name": parts[1]})
                 continue
 
     if not gpus:
+        if unified:
+            # Unified-memory CUDA box: report the GPU backed by system RAM so the
+            # Cookbook recommends models and serving works. The pool is shared
+            # (not per-GPU discrete VRAM), so report the RAM total once.
+            ram_gb = round(_get_ram_gb(), 1)
+            gpus = [{"index": g["index"], "name": g["name"], "vram_gb": ram_gb} for g in unified]
+            return {
+                "gpu_name": gpus[0]["name"],
+                "gpu_vram_gb": ram_gb,
+                "gpu_count": len(gpus),
+                "gpus": gpus,
+                "gpu_groups": _group_gpus(gpus),
+                "homogeneous": True,
+                "backend": "cuda",
+                "unified_memory": True,
+            }
         return None
     total_vram = sum(g["vram_gb"] for g in gpus)
     groups = _group_gpus(gpus)
diff --git a/tests/test_hwfit_unified_nvidia.py b/tests/test_hwfit_unified_nvidia.py
new file mode 100644
index 0000000..009288e
--- /dev/null
+++ b/tests/test_hwfit_unified_nvidia.py
@@ -0,0 +1,73 @@
+"""Unified-memory NVIDIA detection — Grace Blackwell GB10 / DGX Spark (#1340).
+
+GB10 (and other unified-memory NVIDIA parts) report `nvidia-smi
+--query-gpu=memory.total` as "[N/A]"/"Not Supported" because the GPU shares the
+system LPDDR pool instead of carrying discrete VRAM. The detector did
+`float(memory.total)` and, on the ValueError, `continue`d — dropping the only
+GPU row, so a real GB10 running vLLM was reported as "No GPU" and Cookbook
+recommendations/model-switching broke. These pin that such a device is detected
+as a unified-memory CUDA GPU backed by system RAM, while discrete GPUs are
+unchanged.
+"""
+
+import pytest
+
+from services.hwfit import hardware
+
+
+@pytest.fixture(autouse=True)
+def _local(monkeypatch):
+    monkeypatch.setattr(hardware, "_remote_host", None)
+
+
+def test_gb10_unified_memory_detected_not_dropped(monkeypatch):
+    # Real GB10 nvidia-smi --query-gpu=memory.total,name output: memory is N/A.
+    monkeypatch.setattr(hardware, "_run", lambda cmd: "[N/A], NVIDIA GB10")
+    monkeypatch.setattr(hardware, "_get_ram_gb", lambda: 128.0)
+    info = hardware._detect_nvidia()
+    assert info is not None, "GB10 was dropped as 'No GPU'"
+    assert info["gpu_name"] == "NVIDIA GB10"
+    assert info["backend"] == "cuda"
+    assert info["gpu_count"] == 1
+    assert info["unified_memory"] is True
+    assert info["gpu_vram_gb"] == 128.0          # backed by the unified RAM pool
+    assert hardware._last_gpu_error is None
+
+
+def test_detect_system_reports_gb10_as_gpu(monkeypatch):
+    """End-to-end through detect_system: has_gpu True + unified_memory propagated."""
+    monkeypatch.setattr(hardware, "_run", lambda cmd: "[N/A], NVIDIA GB10")
+    monkeypatch.setattr(hardware, "_get_ram_gb", lambda: 128.0)
+    monkeypatch.setattr(hardware, "_get_available_ram_gb", lambda: 120.0)
+    monkeypatch.setattr(hardware, "_get_cpu_count", lambda: 20)
+    monkeypatch.setattr(hardware, "_get_cpu_name", lambda: "NVIDIA Grace")
+    monkeypatch.setattr(hardware, "_detect_apple_silicon", lambda: None)
+    s = hardware.detect_system(fresh=True)
+    assert s["has_gpu"] is True
+    assert s["gpu_name"] == "NVIDIA GB10"
+    assert s["backend"] == "cuda"
+    assert s.get("unified_memory") is True
+
+
+def test_discrete_gpu_unchanged_and_not_unified(monkeypatch):
+    monkeypatch.setattr(hardware, "_run", lambda cmd: "24576, NVIDIA GeForce RTX 4090")
+    info = hardware._detect_nvidia()
+    assert info["gpu_vram_gb"] == 24.0
+    assert info["gpu_count"] == 1
+    assert not info.get("unified_memory")
+
+
+def test_discrete_takes_precedence_over_unified_row(monkeypatch):
+    """A box with a real discrete-VRAM GPU keeps the discrete path; the
+    N/A-memory row is not conflated into a unified pool."""
+    monkeypatch.setattr(hardware, "_run", lambda cmd: "24576, NVIDIA RTX 4090\n[N/A], NVIDIA GB10")
+    info = hardware._detect_nvidia()
+    assert info["gpu_name"] == "NVIDIA RTX 4090"
+    assert info["gpu_count"] == 1
+    assert not info.get("unified_memory")
+
+
+def test_no_gpu_still_none(monkeypatch):
+    """No nvidia-smi output → still None, no spurious unified GPU."""
+    monkeypatch.setattr(hardware, "_run", lambda cmd: None)
+    assert hardware._detect_nvidia() is None