diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py
index f0d06e179f33..eb6ae7df158b 100644
--- a/vllm/model_executor/layers/linear.py
+++ b/vllm/model_executor/layers/linear.py
@@ -217,9 +217,13 @@ def apply(
         x: torch.Tensor,
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
+        weight = layer.weight
+        # Support FP8 weight storage: cast to compute dtype for GEMM
+        if weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+            weight = weight.to(x.dtype)
         if vllm_is_batch_invariant() and current_platform.is_cuda_alike():
-            return linear_batch_invariant(x, layer.weight, bias)
-        return dispatch_unquantized_gemm()(layer, x, layer.weight, bias)
+            return linear_batch_invariant(x, weight, bias)
+        return dispatch_unquantized_gemm()(layer, x, weight, bias)
 
 
 class LinearBase(PluggableLayer):
diff --git a/vllm/model_executor/layers/vocab_parallel_embedding.py b/vllm/model_executor/layers/vocab_parallel_embedding.py
index daaa86bed478..fb9fa0c98e19 100644
--- a/vllm/model_executor/layers/vocab_parallel_embedding.py
+++ b/vllm/model_executor/layers/vocab_parallel_embedding.py
@@ -66,10 +66,18 @@ def apply(
         x: torch.Tensor,
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        return dispatch_unquantized_gemm()(layer, x, layer.weight, bias)
+        weight = layer.weight
+        # Support FP8 weight storage: cast to compute dtype for GEMM
+        if weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+            weight = weight.to(x.dtype)
+        return dispatch_unquantized_gemm()(layer, x, weight, bias)
 
     def embedding(self, layer: torch.nn.Module, input_: torch.Tensor) -> torch.Tensor:
-        return F.embedding(input_, layer.weight)
+        output = F.embedding(input_, layer.weight)
+        # Support FP8 weight storage: cast to compute dtype after lookup
+        if output.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+            output = output.to(torch.get_default_dtype())
+        return output
 
 
 def pad_vocab_size(vocab_size: int, pad_to: int = DEFAULT_VOCAB_PADDING_SIZE) -> int:
@@ -429,6 +437,14 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
                 shape[output_dim] = self.num_embeddings_per_partition
             param.materialize(tuple(shape), dtype=loaded_weight.dtype)
 
+        # If loaded weight is FP8, cast parameter to match so FP8 is
+        # preserved in memory (saves VRAM for e.g. embed_tokens).
+        if (
+            loaded_weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2)
+            and param.data.dtype != loaded_weight.dtype
+        ):
+            param.data = torch.empty_like(param.data, dtype=loaded_weight.dtype)
+
         # If parameter does not have output dim, then it should
         # be copied onto all gpus (e.g. g_idx for act_order gptq).
         if output_dim is None: