diff --git a/modules/sdnq/kernels/openvino_mm.py b/modules/sdnq/kernels/openvino_mm.py
index b5fd9e43d..25ae94b0a 100644
--- a/modules/sdnq/kernels/openvino_mm.py
+++ b/modules/sdnq/kernels/openvino_mm.py
@@ -8,11 +8,11 @@ core = ov.Core()
 
 NPU_MUL = 32 # NPU uses FP16 x INT8 -> FP16 instead of INT8 x INT8 -> INT32 and FP16 output overflows
 OV_DEVICE: str = os.environ.get("SDNQ_OPENVINO_DEVICE", "CPU")
-OV_COMPILED_CACHE: dict[tuple[str, tuple[int,int] | None, tuple[int,int] | None], list[ov.InferRequest, str]] = {}
+OV_COMPILED_CACHE: dict[tuple[str, tuple[int,int] | None, tuple[int,int] | None], tuple[ov.InferRequest, str]] = {}
 core.set_property(OV_DEVICE, {ov_hints.execution_mode: ov_hints.ExecutionMode.ACCURACY})
 
 
-def ov_int_mm(A: torch.Tensor, B: torch.Tensor, infer_request: ov.InferRequest, out_name: str) -> torch.Tensor:
+def ov_int_mm(A: torch.CharTensor, B: torch.CharTensor, infer_request: ov.InferRequest, out_name: str) -> torch.FloatTensor:
     C = torch.empty((A.shape[0], B.shape[-1]), device="cpu", dtype=torch.float32)
     infer_request.set_tensor("A", ov.Tensor(A.detach().contiguous().to("cpu").numpy(), shared_memory=True))
     infer_request.set_tensor("B", ov.Tensor(B.detach().contiguous().to("cpu").numpy(), shared_memory=True))
diff --git a/modules/sdnq/quant_utils.py b/modules/sdnq/quant_utils.py
index 0ea0c1b52..94ba60c45 100644
--- a/modules/sdnq/quant_utils.py
+++ b/modules/sdnq/quant_utils.py
@@ -75,7 +75,7 @@ def apply_svdquant(weight: torch.FloatTensor, rank: int = 32, niter: int = 8, dt
 
 
 @devices.inference_context()
-def build_hadamard_n2(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None):
+def build_hadamard_n2(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None) -> torch.FloatTensor:
     current_size = 2
     H = H_N2 = torch.tensor([[1, 1], [1, -1]], dtype=dtype, device=device)
     while current_size < n:
@@ -87,7 +87,7 @@ def build_hadamard_n2(n: int, dtype: torch.dtype | None = None, device: torch.de
 
 
 @devices.inference_context()
-def build_hadamard_n4(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None):
+def build_hadamard_n4(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None) -> torch.FloatTensor:
     current_size = 4
     H = H_N4 = torch.tensor([[ 1,  1,  1, -1], [ 1,  1, -1,  1], [ 1, -1,  1,  1], [-1,  1,  1,  1]], dtype=dtype, device=device)
     while current_size < n:
@@ -99,7 +99,7 @@ def build_hadamard_n4(n: int, dtype: torch.dtype | None = None, device: torch.de
 
 
 @devices.inference_context()
-def build_hadamard(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None):
+def build_hadamard(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None) -> torch.FloatTensor:
     if math.log(n, 4).is_integer():
         return build_hadamard_n4(n, device=device, dtype=dtype)
     elif math.log(n, 2).is_integer():
@@ -111,9 +111,9 @@ def build_hadamard(n: int, dtype: torch.dtype | None = None, device: torch.devic
 # 256x256 Hadamard matrix is just 256 KB at FP32
 # And is the exact same matrix on all model layers
 # So we can safely cache a single one
-HADAMARD_MATRIX_CACHE = {}
+HADAMARD_MATRIX_CACHE: dict[tuple[int, torch.device, torch.dtype], torch.FloatTensor] = {}
 @devices.inference_context()
-def get_hadamard(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None):
+def get_hadamard(n: int, dtype: torch.dtype | None = None, device: torch.device | None = None) -> torch.FloatTensor:
     device = devices.normalize_device(device)
     H_key = (n, device, dtype)
     H = HADAMARD_MATRIX_CACHE.get(H_key, None)