diff --git a/helm/modules/product_mice.py b/helm/modules/product_mice.py
index e1a9c8c..0628716 100644
--- a/helm/modules/product_mice.py
+++ b/helm/modules/product_mice.py
@@ -13,7 +13,6 @@ bias-update .float() calls. No NEW manual fp32 promotions.
 import torch
 from torch import nn
 import torch.nn.functional as F
-import torch.distributed as dist
 
 from helm.hypercore.manifolds.product_lorentz import ProductLorentz
 from helm.hypercore.nn.linear.product_lorentz_linear import ProductLorentzLinear
@@ -149,15 +148,13 @@ class ProductLorentzMoE(nn.Module):
         self.dim = args.dim
         self.manifold = manifold
         self.n_factors = manifold.n_factors
-        world_size = dist.get_world_size() if dist.is_initialized() else 1
-        if args.n_routed_experts % world_size:
-            raise AssertionError(f'n_routed_experts must be divisible by world_size={world_size}')
         if args.n_activated_experts != 2 or args.n_shared_experts != 1:
             raise NotImplementedError('Validated HELM-D product MiCE configuration requires top2 routed and one shared expert')
         if self.dim % self.n_factors:
             raise ValueError('Model width must divide evenly into Lorentz factors')
         self.n_routed_experts = args.n_routed_experts
-        self.n_local_experts = args.n_routed_experts  # match supplied HELM code
+        # Ordinary DDP replicates all experts on every rank, regardless of world size.
+        self.n_local_experts = args.n_routed_experts
         self.n_activated_experts = args.n_activated_experts
         self.experts_start_idx = 0
         self.experts_end_idx = self.n_routed_experts