diff --git a/troubleshooting/overview.mdx b/troubleshooting/overview.mdx
index 0cd704b92..c63a0c9d8 100644
--- a/troubleshooting/overview.mdx
+++ b/troubleshooting/overview.mdx
@@ -283,6 +283,31 @@ nvidia-smi
 nvidia-smi --query-gpu=driver_version --format=csv
 ```
 
+**Blackwell GPUs (`sm_103`): generation fails at the first attention layer:**
+
+On Blackwell-generation NVIDIA GPUs, PyTorch's default scaled dot-product attention (SDPA) can
+select the cuDNN backend, which has no valid plan for this architecture:
+
+```
+cuDNN Frontend error: No valid execution plans built
+```
+
+With FLUX this hits the very first attention block in the UNet, so no image is ever produced.
+
+```bash
+# Use a hand-written attention implementation that bypasses SDPA and cuDNN
+python main.py --use-split-cross-attention
+
+# Blackwell also requires a CUDA 12.8 or newer PyTorch build;
+# builds compiled against cu121 will not run on these cards at all
+```
+
+<Note>
+Disabling the cuDNN SDPA backend programmatically before startup — for example calling
+`torch.backends.cuda.enable_cudnn_sdp(False)` from a `sitecustomize.py` — does not work, because
+the flag is not retained through CUDA initialization. The launch flag is the reliable fix.
+</Note>
+
 ### AMD GPU Issues
 
 **ROCm support (Linux only):**