diff --git a/doc/notebooks/02_tutorial_boundary_setup.ipynb b/doc/notebooks/02_tutorial_boundary_setup.ipynb
index 1cb96d0a20882d6782f1397bfe0f4c4b7ff031b5..0e1c34106976a0cb0c9f1556ad1667f486e0f983 100644
--- a/doc/notebooks/02_tutorial_boundary_setup.ipynb
+++ b/doc/notebooks/02_tutorial_boundary_setup.ipynb
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "lbm_config = LBMConfig(stencil=Stencil.D3Q19, method=Method.SRT, relaxation_rate=1.9, force=(1e-6, 0, 0))\n",
+    "lbm_config = LBMConfig(stencil=Stencil.D3Q27, method=Method.SRT, relaxation_rate=1.9, force=(1e-6, 0, 0))\n",
     "config = CreateKernelConfig(target=Target.CPU)\n",
     "\n",
     "sc1 = LatticeBoltzmannStep(domain_size=domain_size,\n",
@@ -98,14 +98,12 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 3200x1200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -128,14 +126,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 3200x1200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -164,11 +160,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "lbm_config = LBMConfig(stencil=Stencil.D3Q27, method=Method.SRT, relaxation_rate=1.9)\n",
     "config = CreateKernelConfig(target=Target.CPU)\n",
     "# config = CreateKernelConfig(target=Target.GPU, gpu_indexing_params={'block_size': (128, 1, 1)})\n",
     "\n",
     "sc2 = LatticeBoltzmannStep(domain_size=domain_size,\n",
-    "                           lbm_config=LBMConfig(method=Method.SRT, relaxation_rate=1.9),\n",
+    "                           lbm_config=lbm_config,\n",
     "                           config=config)\n"
    ]
   },
@@ -266,14 +263,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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nny3deTdkyJDSxfeKxk6dOjUzZsxIsniLMXfeAQBQCccdd1ySxSvI/f73v1/qteuuu6503Nxb71599dWl4z333HOFgb4lPvzhDzf5+hI33HBDKZi4xRZbLPXF2bK23nrr5VbbWFMLFiworRzSrl27XHnllamtbXqzie9///ulLbyWrJQCAAArMm/evCxcuLD0eOONN17juTbaaKOlHr/++utrPFdb8tRTT2XChAmlxz/5yU+aXLX7/PPPT7du3ZIs/vu/4YYblhuzqi14lzzXqVOnfPGLX8z666+/yrHLzgsA0JYI9QEUTMeOHXPooYc2OWbXXXctHU+bNq2FK/qvxhfCjYOFK3PiiSeWVvFYURAvafpCfdm76cod6yIdAIBKOe6440rhssYhvvr6+vz6179OsjgAN3jw4GY9b+P+eEmwsCnlhAobz3n00Uevcrve5goqPvzww5k9e3aSxasfbrnllqt8z+abb166YeiJJ57Iv//972apBQCA6rPsKnGdO3de47mWvTl/3rx5azxXW9L4WmGXXXbJbrvt1uT4zp0755hjjlnh+5dY1c4+jXf1WW+99Zrc2cf3BQBANWj6NmcAWt3222/f5B1tSbLJJpuUjv/zn/+0dElJklmzZpW+WEsWXzivSvfu3bPddtvl6aefTpI88sgjy20pPGTIkNL2WPfee29OO+200mtLLrzXW2+9DBw4MOuss066du2aefPmLTfWnXcAABTBlltumX333Tfjx4/PXXfdlZdffjmbbrpp/vCHP5SCZuWE7lZHQ0ND/vGPf5QeDxo0aJXvGThwYGpqapZbTbuxxx57bLXm3GabbbLJJpvktddeW+XYpjz44IOl4zlz5uTLX/5yWe9b8vfb0NCQWbNmZcMNN1yrOgAAqE5LVnhb4q233lrjud58882lHnft2nWN52pLHn300dJxuTcsDR48OJdffnmSxd8VLKtnz57p379/nn766bz88st56qmnssMOOyRZvKvPSy+9lOS/2+nuv//+GTdu3HJjp0yZkpkzZyZZvKvP5ptvvoafEgCgsoT6AApmgw02WOWYxitkNN4moCW9+uqrpeNOnTqlR48eZb2vb9++pVDfnDlzlnu9qbvvGt95tyTouPfee+eOO+5Y6dhl5wQAgNZ2/PHHZ/z48Vm0aFGuv/76fO1rXyut2ldTU9Psob7//Oc/mT9/funxFltsscr3dO3aNRtssEGTK9o1vgYoZ84k6dOnz1qH+pZ8WZckzzzzzApX/F6VuXPnrlUNAABUr65du6a2trb0u/W12TJ32b5zbbbybUsaXyuUs7J2svi7giVW9F1Bsvh3+0u+T7jvvvtKQb0Vrby37HcLTY0FAGiLbL8LUDBLtuoqmsZ3HK7OdgSNxy67rUGy+O67JRfbs2fPzpNPPplkxXfeJf+9CG889rnnnivdebfddtu58w4AgIo68sgjs9566yVZvAXv7Nmzc+eddyZZfJNKv379mvV8y64OsuTcq7Kqvr7xvM01ZzmaYzXy1rr5CQCAtqlxEO2JJ55Y43mWfW+5Abe2bk2+L1jVdwXJ0t8FNN6dZ9ldfZJkt912K62M2Hhs4+PG8wEAtDVCfQCUpUuXLqXj1dmOoPHYZbc1WKLx3XJLLrhXduG9oot6d94BAFAk66+/fj75yU8mSf7xj3/k61//eilkdvzxxzf7+Rr36kny9ttvl/W+VfX1jedtrjnL0fjLvtNPPz0NDQ2r/cd1AQAATWm8Zexf//rXNZ6n8Xu33HLL9O7de63qeu+999bq/a1lTb4vWN3vCu677740NDSUjpPFN0kt2clonXXWyT777LPcWLv6AADVQqgPgLJ07969dPzOO++sdHn8ZU2fPr103K1btxWOWVGob8lFepcuXTJgwIDS67vuumtpi+IVBQBdpAMAUASNw3tjxoxJknTs2DFHHnlks59rgw02SPv27UuPX3zxxVW+Z968eatcEa/xNUA5cybJjBkzyhrXlJ49e5aOn3vuubWeDwAAltX45vGZM2dmwoQJqz3HW2+9lVtvvXWFcy6xJICWlLeadHOsWt0a1uRaoZzvCnr06FHa2efVV1/Nv/71rzzzzDOpq6tLsvzf8ZLvA5aMffbZZzNr1qwki3f12Wyzzcr7QAAABSTUB0BZevXqlR49epQeP/DAA6t8z2uvvZZnnnmm9Hi33XZb4bjGQbzx48enoaGhdDfd4MGDU1tbW3q98d13y45ddi4AAKiUgw46KJtvvvlSzw0bNqx0g0pzqqmpyc4771x6PHny5FW+56GHHiqtZLEyH/rQh1ZrzilTpuS1115b5bhVGTRoUOl4/Pjxqa+vX+s5AQCgsSOPPDIbbbRR6fGll1662nP87Gc/W2ob2lNPPXW5MY1XpCunV/7nP/+5yjE1NTVlVthydt1119JxOd8VJMmkSZNKxyv7riBZfrW+pnbqaRzyW9VYAIC2RqgPgLI1vkBestpIU6699trSdgGbb755tt9++xWOa3z33Zw5c/Lb3/52pXfeJf+9GF8y9qWXXkrizjsAAIpjnXXWybHHHrvUcy2x9e4SjfvmX/3qV6scf+21167WnL/+9a+zYMGCtZ6zHIMHD86GG26YJHnzzTfz85//vFnmBQCAJTp37pwvfvGLpce33nprfve735X9/unTp2fkyJGlx3vvvXf23HPP5cZttdVWpePHHnusyTkffvjhvPDCC6s8d8eOHUvHq+rRW8oBBxxQOn700Ufz+OOPNzn+nXfeyY033rjC9y9r2Z19Gu/qs8ceeyw1dtmdfezqAwBUE6E+AMrW+E7DW265JXfeeedKx86YMSPf/e53l3pvU3cQNv7CcNSoUSt8fk3GAgBApXzrW9/K3/72t9Kfj370oy12rpNOOql0PHny5Fx//fUrHfvAAw/khhtuWOWcxx57bDp16pRkcX//gx/8YKVjp06dmssuu2w1Kl65Dh065PTTTy89Puecc8pasWSJV155pVnqAACgun3rW9/KjjvuWHp83HHHlbUN78svv5yDDz64tErfeuutt9IbUQYOHFg6buommIULF+YrX/lKWXVvsskmpeMlW822tv79+2ffffctPR4xYkSTAcNvf/vbmT17dpKka9euy90A1diyO/ss2alnn332WWpXnyRp165dqY7GY5edBwCgLRLqA6Bs+++/f4YOHVp6fOSRR+amm25abtyjjz6aAw88MHPnzk2S9OnTJ//7v//b5NyNL7CfeOKJJIu3Jth9992XG/uhD32otHLHkrHLzgEAAJW24YYbZo899ij9WWeddVrsXP37989xxx1Xevy5z31uhcG+u+++O8OGDct7772Xddddt8k5N9xww5x55pmlx9/+9rdz6aWXllbjXuLRRx/NwQcfnLfeeivt27dfy0+y2Ne+9rXstNNOSZI33ngje++9d37+859n/vz5Kxz/2muvZfTo0dl9991z0UUXNUsNAABUt06dOuXXv/51aaW3t99+OwcddFC+9a1vrXCr3Pnz5+e6667LLrvskmeffTbJ4q1wr7jiivTv33+F5/jMZz6Tdu0Wfx374IMP5hvf+EYWLVq01JiZM2fm0EMPzQMPPJAOHTqssu4PfvCDpeM//elP+c9//lPeB25mF1xwQeka5/7778+nPvWpUnBvifnz5+db3/pWLrnkktJz5557brp06bLSeXv06FEKW7722mulXX1W9vv/Jc83Hrv99tvb1QcAaPNqVz0EAP7rmmuuyeDBgzN16tS8+eabOeqoo7Lttttm0KBBad++fZ566qlMnjw5DQ0NSRZvYzB27NhSCG9lVnRBvs8++6zwi88ld9/94Q9/WOUcAADwfnHZZZflwQcfzJQpU/Luu+/mf/7nfzJq1KjsueeeWWeddfL444/n0UcfTZKcfvrpueWWWzJ9+vQm5/zWt76Vu+66q9Tjf+1rX8tll12WffbZJ+utt16efvrpPPDAA2loaMgnP/nJzJ07d6nVMdZUly5d8oc//CEHHXRQXnjhhcybNy+nnHJKzjrrrOy1117p1atXampq8vrrr+epp57KM888UwobWsEbAIByfeADH8jEiRMzdOjQzJw5MwsWLMj555+fH/zgB9lrr72y5ZZbpn379qmrq8sDDzywVICuffv2ue6663L00UevdP4tt9wyp512Wn76058mSS688MKMHTs2++67bzp27JipU6dm0qRJmT9/fg488MBsttlm+dWvftVkzQMGDMgWW2yRF198MS+//HL69++fgw8+ON26dSvtljNgwIAm62oOe+21Vy644IKcddZZSZLbbrstW2yxRfbff//06dMnc+fOzX333Zc5c+aU3nP44YfnjDPOWOXcQ4YMyZNPPrnUcyvr81f0vO8KAIBqINQHwGrp2bNnJk2alGOPPTb33HNPkuS5557Lc889t9zYbbbZJjfccEMGDBiwynm7d++eHXfccakL9aa+jBsyZMhSob7tt98+m2666ep8FAAAqCqbbLJJ7rnnngwbNqwU3ltRr37iiSfmwgsvzC233LLKOTt06JA777wzn/rUp3L33XcnWbySyNixY5cad8ghh2TMmDEZNmxYM32apF+/fnn44Ydz2mmn5eabb05DQ0P+85//5M4771zpezbccMOlVi4BAIBV+cAHPpC///3vOffcczN69OgsXLgwCxcuzP3335/7779/he856KCDcvHFF2eXXXZZ5fyXXHJJpk2bljvuuCNJ8uKLLy4X3Dv00EPzy1/+Mqeffvoq52vXrl2uuOKKHHHEEamvr8/LL7+c6667bqkxJ5xwQouH+pLkzDPPzEYbbZSvfvWrmTdvXurr61fYr6+zzjr58pe/nEsuuaQUPGzKkCFDSkHIZPGWvbvtttsKx+6yyy7ZaKONSjsHLXk/AEBbJ9QHwGrr2bNn7r777vzpT3/KjTfemIkTJ+bll1/OggUL0qNHj+y666755Cc/mf/5n/9Z5ZZeje2///5LhfqauvB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       "text/plain": [
        "<Figure size 2800x1600 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -311,14 +306,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2800x1600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -352,14 +345,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2800x1600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -373,7 +364,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -387,7 +378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/src/lbmpy/lbstep.py b/src/lbmpy/lbstep.py
index 9bafbe6b8917e00e891b5a3fe68f03781f96a864..4b22c9deacd7ad93e737f6510716d557702951a4 100644
--- a/src/lbmpy/lbstep.py
+++ b/src/lbmpy/lbstep.py
@@ -49,6 +49,9 @@ class LatticeBoltzmannStep:
                                                  default_target=target,
                                                  parallel=False)
 
+        if lbm_config:
+            method_parameters['stencil'] = lbm_config.stencil
+
         if 'stencil' not in method_parameters:
             method_parameters['stencil'] = LBStencil(Stencil.D2Q9) \
                 if data_handling.dim == 2 else LBStencil(Stencil.D3Q27)
diff --git a/tests/test_cpu_gpu_equivalence.py b/tests/test_cpu_gpu_equivalence.py
index f519d3f5746582da49e2406eb9ff31848d05be8c..2824d1efbac1b27051f77e0951476de2cea859f0 100644
--- a/tests/test_cpu_gpu_equivalence.py
+++ b/tests/test_cpu_gpu_equivalence.py
@@ -5,8 +5,9 @@ import pytest
 from pystencils import Backend, Target, CreateKernelConfig
 
 from lbmpy.creationfunctions import LBMConfig, LBMOptimisation
+from lbmpy.enums import Method, Stencil
 from lbmpy.scenarios import create_channel
-from lbmpy.enums import Method
+from lbmpy.stencils import LBStencil
 
 
 def run_equivalence_test(domain_size, lbm_config, lbm_opt, config, time_steps=13):
@@ -33,12 +34,14 @@ def run_equivalence_test(domain_size, lbm_config, lbm_opt, config, time_steps=13
 def test_force_driven_channel_short(scenario):
     pytest.importorskip("cupy")
     ds = scenario[0]
+    stencil = LBStencil(Stencil.D2Q9) if len(ds) == 2 else LBStencil(Stencil.D3Q27)
     method = scenario[1]
     compressible = scenario[2]
     block_size = scenario[3]
     field_layout = scenario[4]
 
-    lbm_config = LBMConfig(method=method, compressible=compressible, relaxation_rates=[1.95, 1.9, 1.92, 1.92])
+    lbm_config = LBMConfig(stencil=stencil, method=method,
+                           compressible=compressible, relaxation_rates=[1.95, 1.9, 1.92, 1.92])
     lbm_opt = LBMOptimisation(field_layout=field_layout)
 
     # Different methods
diff --git a/tests/test_float_kernel.py b/tests/test_float_kernel.py
index 6e4171b32dcf9a9b7eb30cd965facc254b6fd9ce..f4fd97bd161575715a201a84bc2ecdac5ed1f3ea 100644
--- a/tests/test_float_kernel.py
+++ b/tests/test_float_kernel.py
@@ -3,8 +3,9 @@ import pytest
 import pystencils as ps
 
 from lbmpy.creationfunctions import create_lb_function, LBMConfig
-from lbmpy.enums import Method
+from lbmpy.enums import Method, Stencil
 from lbmpy.scenarios import create_lid_driven_cavity
+from lbmpy.stencils import LBStencil
 
 
 @pytest.mark.parametrize('double_precision', [False, True])
@@ -28,7 +29,8 @@ def test_creation(double_precision, method_enum):
 @pytest.mark.parametrize('double_precision', [False, True])
 @pytest.mark.parametrize('method_enum', [Method.SRT, Method.CENTRAL_MOMENT, Method.CUMULANT])
 def test_scenario(method_enum, double_precision):
-    lbm_config = LBMConfig(method=method_enum, relaxation_rate=1.45, compressible=True)
+    lbm_config = LBMConfig(stencil=LBStencil(Stencil.D3Q27), method=method_enum,
+                           relaxation_rate=1.45, compressible=True)
     config = ps.CreateKernelConfig(data_type="float64" if double_precision else "float32",
                                    default_number_float="float64" if double_precision else "float32")
     sc = create_lid_driven_cavity((16, 16, 8), lbm_config=lbm_config, config=config)