diff --git a/src/llmcompressor/modeling/llama4.py b/src/llmcompressor/modeling/llama4.py
index 02e3dc8fc..78f9d8ab2 100644
--- a/src/llmcompressor/modeling/llama4.py
+++ b/src/llmcompressor/modeling/llama4.py
@@ -1,8 +1,10 @@
 from typing import Tuple
 
 import torch
-from transformers.models import Llama4Config
-from transformers.models.llama4.configuration_llama4 import Llama4TextConfig
+from transformers.models.llama4.configuration_llama4 import (
+    Llama4Config,
+    Llama4TextConfig,
+)
 from transformers.models.llama4.modeling_llama4 import (
     Llama4TextExperts,
     Llama4TextMLP,
diff --git a/src/llmcompressor/modifiers/awq/base.py b/src/llmcompressor/modifiers/awq/base.py
index 7d5c3671f..6e533cc1a 100644
--- a/src/llmcompressor/modifiers/awq/base.py
+++ b/src/llmcompressor/modifiers/awq/base.py
@@ -465,11 +465,13 @@ def _apply_smoothing(self, model: Module) -> None:
             # Calculates the relative magnitude of the weights within
             # each of the quantization groups, and rescales each group
             # individually so that each group has weights on a 0-1 scale.
-            w_scale = weight.abs() / (weight.abs().amax(dim=1, keepdim=True) + 1e-6)
+            weight.abs_()
+            weight.div_(weight.amax(dim=1, keepdim=True) + 1e-6)
             # Resizes the rescaled weight matrix back up to its original dimensions
-            w_scale = w_scale.view(org_shape)
+            weight = weight.view(org_shape)
             # Gets the average rescaled magnitude for each output channel
-            w_mean = w_scale.mean(0)
+            w_mean = weight.mean(0)
+            del weight
 
             with calibration_forward_context(model), HooksMixin.disable_hooks():
                 # [STEP 3]: Compute output of module
diff --git a/src/llmcompressor/modifiers/awq/mappings.py b/src/llmcompressor/modifiers/awq/mappings.py
index 693406ec3..743f14e85 100644
--- a/src/llmcompressor/modifiers/awq/mappings.py
+++ b/src/llmcompressor/modifiers/awq/mappings.py
@@ -116,9 +116,26 @@ class AWQMapping:
     ),
 ]
 
+# DeepseekV3
+_deepseek_mappings = [
+    AWQMapping(
+        "re:.*input_layernorm$",
+        # Some models use q_proj instead of q_a_proj
+        ["re:.*(q|q_a)_proj$", "re:.*kv_a_proj_with_mqa$"],
+    ),
+    AWQMapping("re:.*q_a_layernorm$", ["re:.*q_b_proj$"]),
+    AWQMapping("re:.*kv_a_layernorm$", ["re:.*kv_b_proj$"]),
+    AWQMapping(
+        "re:.*post_attention_layernorm$",
+        ["re:.*gate_proj$", "re:.*up_proj$"],
+    ),
+    AWQMapping("re:.*up_proj$", ["re:.*down_proj$"]),
+]
+
 AWQ_MAPPING_REGISTRY: Dict[str, list[AWQMapping]] = {
     "CohereForCausalLM": _cohere_mappings,
     "Cohere2ForCausalLM": _cohere_mappings,
+    "DeepseekV3ForCausalLM": _deepseek_mappings,
     "Gemma2ForCausalLM": _gemma_mappings,
     "Gemma3ForCausalLM": _gemma_mappings,
     "Gemma3ForConditionalGeneration": _gemma_mappings,