diff --git a/mlx_lm/generate.py b/mlx_lm/generate.py
index 3573b2640..35111623c 100644
--- a/mlx_lm/generate.py
+++ b/mlx_lm/generate.py
@@ -5,6 +5,7 @@
 import copy
 import functools
 import json
+import logging
 import sys
 import time
 from collections import deque
@@ -40,8 +41,9 @@
 )
 from .sample_utils import make_sampler
 from .tokenizer_utils import TokenizerWrapper
-from .utils import does_model_support_input_embeddings, load
+from .utils import does_model_support_input_embeddings, get_total_parameters, load
 
+logger = logging.getLogger(__name__)
 DEFAULT_PROMPT = "hello"
 DEFAULT_MAX_TOKENS = 100
 DEFAULT_TEMP = 0.0
@@ -304,6 +306,40 @@ def maybe_quantize_kv_cache(prompt_cache, quantized_kv_start, kv_group_size, kv_
             prompt_cache[e] = c.to_quantized(group_size=kv_group_size, bits=kv_bits)
 
 
+def _warn_speculative_moe(model: nn.Module, draft_model: nn.Module) -> None:
+    """Warn if speculative decoding is unlikely to help for MoE models."""
+    model_config = getattr(model, "args", None)
+    if model_config is None:
+        return
+
+    num_experts = getattr(model_config, "num_experts", None) or getattr(
+        model_config, "num_local_experts", None
+    )
+    num_experts_per_tok = getattr(model_config, "num_experts_per_tok", None) or getattr(
+        model_config, "num_experts_per_token", None
+    )
+
+    if num_experts == 0:
+        return
+
+    draft_params = get_total_parameters(draft_model)
+    target_params = get_total_parameters(model)
+    active_params = target_params * (num_experts_per_tok / num_experts)
+    ratio = active_params / draft_params
+
+    if (
+        ratio < 4.0
+    ):  # warn if draft model is >25% of active params (empirically shows slowdown)
+        active_b = active_params / 1e9
+        draft_b = draft_params / 1e9
+        logger.warning(
+            f"Target model active parameters ({active_b:.1f}B) are close to "
+            f"draft model size ({draft_b:.1f}B). Speculative decoding may hurt "
+            f"throughput for MoE architectures. Consider benchmarking with and "
+            f"without --draft-model."
+        )
+
+
 def generate_step(
     prompt: mx.array,
     model: nn.Module,
@@ -2057,6 +2093,7 @@ def main():
         draft_model, draft_tokenizer = load(args.draft_model)
         if draft_tokenizer.vocab_size != tokenizer.vocab_size:
             raise ValueError("Draft model tokenizer does not match model tokenizer.")
+        _warn_speculative_moe(model, draft_model)
     else:
         draft_model = None
     sampler = make_sampler(