diff --git a/vllm/model_executor/models/nemotron_h.py b/vllm/model_executor/models/nemotron_h.py
index 8915d8172c56..0d303e3eb8a4 100644
--- a/vllm/model_executor/models/nemotron_h.py
+++ b/vllm/model_executor/models/nemotron_h.py
@@ -875,6 +875,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.hidden_size,
+            quant_config=self.quant_config,
             prefix=maybe_prefix(prefix, "lm_head"),
         )
 
diff --git a/vllm/model_executor/models/nemotron_h_mtp.py b/vllm/model_executor/models/nemotron_h_mtp.py
index fe737438c30f..1f0693c5ae5e 100644
--- a/vllm/model_executor/models/nemotron_h_mtp.py
+++ b/vllm/model_executor/models/nemotron_h_mtp.py
@@ -11,6 +11,7 @@
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.config.parallel import ParallelConfig
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     fused_moe_make_expert_params_mapping,
 )
@@ -36,6 +37,8 @@
     NemotronHMoEDecoderLayer,
 )
 
+logger = init_logger(__name__)
+
 
 class NemotronHMTPAttentionDecoderLayer(NemotronHAttentionDecoderLayer):
     def __init__(
@@ -242,6 +245,35 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         # Total number of physical layers = num_steps * pattern_len
         total_layers = self.num_mtp_layers * self.pattern_len
+
+        quant_config = vllm_config.quant_config
+        if (
+            quant_config is not None
+            and quant_config.get_name() == "compressed-tensors"
+            and hasattr(quant_config, "ignore")
+        ):
+            num_experts = getattr(config, "n_routed_experts", None)
+            if getattr(config, "model_type", None) == "nemotron_h_puzzle":
+                num_experts = getattr(config, "mtp_n_routed_experts", num_experts)
+            if num_experts:
+                extra: list[str] = []
+                for i in range(total_layers):
+                    if self.pattern_str[i % self.pattern_len] != "E":
+                        continue
+                    for eid in range(num_experts):
+                        for proj in ("gate_proj", "up_proj", "down_proj"):
+                            extra.append(
+                                f"{prefix}.layers.{i}.mixer.experts.{eid}.{proj}"
+                            )
+                new_entries = [n for n in extra if n not in quant_config.ignore]
+                quant_config.ignore.extend(new_entries)
+                if new_entries:
+                    logger.info(
+                        "NemotronH-MTP: extended compressed-tensors ignore "
+                        "with %d per-expert MTP linears (BF16 in the checkpoint)",
+                        len(new_entries),
+                    )
+
         for i in range(total_layers):
             step_rel_idx = i % self.pattern_len
 
@@ -346,6 +378,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.lm_head = ParallelLMHead(
             self.config.vocab_size,
             self.config.hidden_size,
+            quant_config=self.quant_config,
             prefix=maybe_prefix(prefix, "lm_head"),
         )