Dot product of two matrices in NLP how can i get this error be solved

from sklearn.metrics.pairwise import linear_kernel
sim_matrix = linear_kernel(tfidf_matrix, tfidf_matrix)

when I try to get dot product I am getting this errro

MemoryError                               Traceback (most recent call last)
ipython-input-19-2c4d43d4a89e in module
      1 from sklearn.metrics.pairwise import linear_kernel
---- 2 sim_matrix = linear_kernel(tfidf_matrix, tfidf_matrix)

~\anaconda3\lib\site-packages\sklearn\metrics\pairwise.py in linear_kernel(X, Y, dense_output)
   1002     
   1003     X, Y = check_pairwise_arrays(X, Y)
- 1004     return safe_sparse_dot(X, Y.T, dense_output=dense_output)
   1005 
   1006 

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
--- 72         return f(**kwargs)
     73     return inner_f
     74 

~\anaconda3\lib\site-packages\sklearn\utils\extmath.py in safe_sparse_dot(a, b, dense_output)
    151             ret = np.dot(a, b)
    152     else:
-- 153         ret = a @ b
    154 
    155     if (sparse.issparse(a) and sparse.issparse(b)

~\anaconda3\lib\site-packages\scipy\sparse\base.py in __matmul__(self, other)
    558             raise ValueError(Scalar operands are not allowed, 
    559                              use '*' instead)
-- 560         return self.__mul__(other)
    561 
    562     def __rmatmul__(self, other):

~\anaconda3\lib\site-packages\scipy\sparse\base.py in __mul__(self, other)
    478             if self.shape[1] != other.shape[0]:
    479                 raise ValueError('dimension mismatch')
-- 480             return self._mul_sparse_matrix(other)
    481 
    482         # If it's a list or whatever, treat it like a matrix

~\anaconda3\lib\site-packages\scipy\sparse\compressed.py in _mul_sparse_matrix(self, other)
    514 
    515         indptr = np.empty(major_axis + 1, dtype=idx_dtype)
-- 516         indices = np.empty(nnz, dtype=idx_dtype)
    517         data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
    518 

MemoryError: Unable to allocate 2.04 GiB for an array with shape (546860044,) and data type int32