diff --git a/glue/sample/src/sinter/_decoding_fusion_blossom.py b/glue/sample/src/sinter/_decoding_fusion_blossom.py
index bcf45cf09..966b69a4f 100644
--- a/glue/sample/src/sinter/_decoding_fusion_blossom.py
+++ b/glue/sample/src/sinter/_decoding_fusion_blossom.py
@@ -1,19 +1,58 @@
 import math
 import pathlib
-from typing import Callable, List, TYPE_CHECKING
-from typing import Tuple
+from typing import Callable, List, TYPE_CHECKING, Tuple
 
 import numpy as np
 import stim
 
-from sinter._decoding_decoder_class import Decoder
+from sinter._decoding_decoder_class import Decoder, CompiledDecoder
 
 if TYPE_CHECKING:
     import fusion_blossom
 
 
+class FusionBlossomCompiledDecoder(CompiledDecoder):
+    def __init__(self, solver: 'fusion_blossom.SolverSerial', fault_masks: 'np.ndarray', num_dets: int, num_obs: int):
+        self.solver = solver
+        self.fault_masks = fault_masks
+        self.num_dets = num_dets
+        self.num_obs = num_obs
+
+    def decode_shots_bit_packed(
+            self,
+            *,
+            bit_packed_detection_event_data: 'np.ndarray',
+    ) -> 'np.ndarray':
+        num_shots = bit_packed_detection_event_data.shape[0]
+        predictions = np.zeros(shape=(num_shots, self.num_obs), dtype=np.uint8)
+        import fusion_blossom
+        for shot in range(num_shots):
+            dets_sparse = np.flatnonzero(np.unpackbits(bit_packed_detection_event_data[shot], count=self.num_dets, bitorder='little'))
+            syndrome = fusion_blossom.SyndromePattern(syndrome_vertices=dets_sparse)
+            self.solver.solve(syndrome)
+            prediction = int(np.bitwise_xor.reduce(self.fault_masks[self.solver.subgraph()]))
+            predictions[shot] = np.packbits(prediction, bitorder='little')
+            self.solver.clear()
+        return predictions
+
+
 class FusionBlossomDecoder(Decoder):
     """Use fusion blossom to predict observables from detection events."""
+
+    def compile_decoder_for_dem(self, *, dem: 'stim.DetectorErrorModel') -> CompiledDecoder:
+        try:
+            import fusion_blossom
+        except ImportError as ex:
+            raise ImportError(
+                "The decoder 'fusion_blossom' isn't installed\n"
+                "To fix this, install the python package 'fusion_blossom' into your environment.\n"
+                "For example, if you are using pip, run `pip install fusion_blossom`.\n"
+            ) from ex
+
+        solver, fault_masks = detector_error_model_to_fusion_blossom_solver_and_fault_masks(dem)
+        return FusionBlossomCompiledDecoder(solver, fault_masks, dem.num_detectors, dem.num_observables)
+
+
     def decode_via_files(self,
                          *,
                          num_shots: int,
@@ -36,7 +75,6 @@ def decode_via_files(self,
         error_model = stim.DetectorErrorModel.from_file(dem_path)
         solver, fault_masks = detector_error_model_to_fusion_blossom_solver_and_fault_masks(error_model)
         num_det_bytes = math.ceil(num_dets / 8)
-
         with open(dets_b8_in_path, 'rb') as dets_in_f:
             with open(obs_predictions_b8_out_path, 'wb') as obs_out_f:
                 for _ in range(num_shots):