Support 1650, 1660

bitsandbytes/autograd/_functions.py

@@ -221,6 +221,17 @@ bmm_cublas = MatMul8bit.apply
 matmul_cublas = MatMul8bit.apply
 
 
+def supports_igemmlt(device: torch.device) -> bool:
+    """check if this device supports the optimized int8 kernel"""
+    if torch.cuda.get_device_capability(device=device) < (7, 5):
+        return False
+    device_name = torch.cuda.get_device_name(device=device)
+    nvidia16_models = ('GTX 1630', 'GTX 1650', 'GTX 1660')  # https://en.wikipedia.org/wiki/GeForce_16_series
+    if any(model_name in device_name for model_name in nvidia16_models):
+        return False  # these devices are technically cuda 7.5-capable, but they lack tensor cores
+    return True
+
+
 @dataclass
 class MatmulLtState:
     tile_indices: Optional[torch.Tensor] = None
@@ -270,7 +281,7 @@ class MatMul8bitLt(torch.autograd.Function):
 
     @staticmethod
     def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
-        using_igemmlt = torch.cuda.get_device_capability(device=A.device) >= (7, 5) and not state.force_no_igemmlt
+        using_igemmlt = supports_igemmlt(A.device) and not state.force_no_igemmlt
         # default of pytorch behavior if inputs are empty
         ctx.is_empty = False
         if prod(A.shape) == 0: