diff --git a/launcher/src/main.rs b/launcher/src/main.rs
index e45b83a3..978af49f 100644
--- a/launcher/src/main.rs
+++ b/launcher/src/main.rs
@@ -342,9 +342,18 @@ struct Args {
 
     /// Whether you want to compile the model into a CUDA graph.
     /// This will speed up decoding but increase GPU memory usage.
-    #[clap(long, env, value_enum)]
+    /// Only use either `--compile` or `--eager`. Using both at the same time will
+    /// result in an error.
+    #[clap(default_value = "true", long, env, value_enum)]
     compile: bool,
 
+    /// Whether you want to run the model in eager mode, without
+    /// CUDA mode compilation, or run it with compilation.
+    /// Only use either `--compile` or `--eager`. Using both at the same time will
+    /// result in an error.
+    #[clap(default_value = "false", long, env, value_enum)]
+    eager: bool,
+
     // The maximum batch size past which CUDA graphs are disabled.
     #[clap(default_value = "128", long, env)]
     compile_max_batch_size: usize,
@@ -656,6 +665,7 @@ fn shard_manager(
     adapter_source: String,
     quantize: Option<Quantization>,
     compile: bool,
+    eager: bool,
     compile_max_batch_size: usize,
     compile_max_rank: usize,
     compile_batch_size: usize,
@@ -738,10 +748,14 @@ fn shard_manager(
     }
 
     // CUDA graph compilation
-    if compile {
+    if compile && !eager {
         shard_args.push("--compile".to_string());
     }
 
+    if (compile && eager) || (!compile && !eager) {
+        panic!("Cannot use both --compile and --eager at the same time.");
+    }
+
     // Speculative decoding
     if let Some(speculative_tokens) = speculative_tokens {
         shard_args.push("--speculative-tokens".to_string());
@@ -1303,6 +1317,7 @@ fn spawn_shards(
         let otlp_endpoint = args.otlp_endpoint.clone();
         let quantize = args.quantize;
         let compile = args.compile;
+        let eager = args.eager;
         let compile_max_batch_size = args.compile_max_batch_size;
         let compile_max_rank = args.compile_max_rank;
         let compile_batch_size = args.compile_batch_size;
@@ -1335,6 +1350,7 @@ fn spawn_shards(
                 adapter_source,
                 quantize,
                 compile,
+                eager,
                 compile_max_batch_size,
                 compile_max_rank,
                 compile_batch_size,
diff --git a/server/lorax_server/models/flash_causal_lm.py b/server/lorax_server/models/flash_causal_lm.py
index 92937511..b8b3dbc6 100644
--- a/server/lorax_server/models/flash_causal_lm.py
+++ b/server/lorax_server/models/flash_causal_lm.py
@@ -1181,7 +1181,10 @@ def __init__(
             SLIDING_WINDOW = sliding_window
             SLIDING_WINDOW_BLOCKS = math.ceil(sliding_window / BLOCK_SIZE)
 
-        self.compile = compile
+        self.compile = compile and self.supports_cuda_graph_compilation
+        if compile and not self.supports_cuda_graph_compilation:
+            logger.info("Model does not support CUDA graph compilation. Skipping compilation.")
+
         self.model_graph_wrapper: GraphCache = None
         self.kv_cache = []
 
diff --git a/server/lorax_server/models/model.py b/server/lorax_server/models/model.py
index 696dc2e8..b29338f7 100644
--- a/server/lorax_server/models/model.py
+++ b/server/lorax_server/models/model.py
@@ -228,6 +228,10 @@ def check_initialized(self):
                 f"found uninitialized parameters in model {self.__class__.__name__}: {uninitialized_parameters}"
             )
 
+    @property
+    def supports_cuda_graph_compilation(self) -> bool:
+        return True
+
     @property
     def supports_adapter_loading(self) -> bool:
         return False