diff --git a/README.md b/README.md
index 7c7cd3a1..801edd73 100644
--- a/README.md
+++ b/README.md
@@ -448,6 +448,36 @@ python -m neural_lam.train_model --model hi_lam_parallel --graph hierarchical ..
 
 Checkpoint files for our models trained on the MEPS data are available upon request.
 
+### High Performance Computing
+
+The training script can be run on a cluster with multiple GPU-nodes. Neural LAM is set up to use PyTorch Lightning's `DDP` backend for distributed training.
+The code can be used on systems both with and without slurm. If the cluster has multiple nodes, set the `--num_nodes` argument accordingly.
+
+Using SLURM, the job can be started with `sbatch slurm_job.sh` with a shell script like the following.
+```
+#!/bin/bash -l
+#SBATCH --job-name=Neural-LAM
+#SBATCH --time=24:00:00
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=4
+#SBATCH --gres:gpu=4
+#SBATCH --partition=normal
+#SBATCH --mem=444G
+#SBATCH --no-requeue
+#SBATCH --exclusive
+#SBATCH --output=lightning_logs/neurallam_out_%j.log
+#SBATCH --error=lightning_logs/neurallam_err_%j.log
+
+# Load necessary modules or activate environment, for example:
+conda activate neural-lam
+
+srun -ul python -m neural_lam.train_model \
+    --config_path /path/to/config.yaml \
+    --num_nodes $SLURM_JOB_NUM_NODES
+```
+
+When using on a system without SLURM, where all GPU's are visible, it is possible to select a subset of GPU's to use for training with the `devices` cli argument, e.g. `--devices 0 1` to use the first 2 GPU's.
+
 ## Evaluate Models
 Evaluation is also done using `python -m neural_lam.train_model --config_path <config-path>`, but using the `--eval` option.
 Use `--eval val` to evaluate the model on the validation set and `--eval test` to evaluate on test data.