diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py
index 3a8201a5..fe858eb0 100644
--- a/ultralytics/utils/checks.py
+++ b/ultralytics/utils/checks.py
@@ -669,8 +669,22 @@ def check_amp(model):
     from ultralytics.utils.torch_utils import autocast
 
     device = next(model.parameters()).device  # get model device
+    prefix = colorstr("AMP: ")
     if device.type in {"cpu", "mps"}:
         return False  # AMP only used on CUDA devices
+    else:
+        # GPUs that have issues with AMP
+        pattern = re.compile(
+            r"(nvidia|geforce|quadro|tesla).*?(1660|1650|1630|t400|t550|t600|t1000|t1200|t2000|k40m)", re.IGNORECASE
+        )
+
+        gpu = torch.cuda.get_device_name(device)
+        if bool(pattern.search(gpu)):
+            LOGGER.warning(
+                f"{prefix}checks failed ❌. AMP training on {gpu} GPU may cause "
+                f"NaN losses or zero-mAP results, so AMP will be disabled during training."
+            )
+            return False
 
     def amp_allclose(m, im):
         """All close FP32 vs AMP results."""
@@ -683,7 +697,6 @@ def check_amp(model):
         return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.5)  # close to 0.5 absolute tolerance
 
     im = ASSETS / "bus.jpg"  # image to check
-    prefix = colorstr("AMP: ")
     LOGGER.info(f"{prefix}running Automatic Mixed Precision (AMP) checks...")
     warning_msg = "Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False."
     try: