diff --git a/backends/v2/src/backend.rs b/backends/v2/src/backend.rs
index bc264138d..cfe87f98f 100644
--- a/backends/v2/src/backend.rs
+++ b/backends/v2/src/backend.rs
@@ -104,6 +104,10 @@ impl Backend for BackendV2 {
         }
         .is_ok()
     }
+
+    fn start_health(&self) -> bool {
+        true
+    }
 }
 
 /// Batching logic
diff --git a/backends/v3/src/backend.rs b/backends/v3/src/backend.rs
index 7ae794a09..736301b33 100644
--- a/backends/v3/src/backend.rs
+++ b/backends/v3/src/backend.rs
@@ -111,6 +111,10 @@ impl Backend for BackendV3 {
         }
         .is_ok()
     }
+
+    fn start_health(&self) -> bool {
+        true
+    }
 }
 
 /// Batching logic
diff --git a/backends/v3/src/client/sharded_client.rs b/backends/v3/src/client/sharded_client.rs
index 6d4e207bb..4701c5600 100644
--- a/backends/v3/src/client/sharded_client.rs
+++ b/backends/v3/src/client/sharded_client.rs
@@ -217,8 +217,8 @@ impl Health for ShardedClient {
             input_chunks: Some(Input {
                 chunks: vec![Chunk::Text("liveness".into()).into()],
             }),
-            truncate: 10,
-            add_special_tokens: true,
+            truncate: 1,
+            add_special_tokens: false,
             prefill_logprobs: false,
             parameters: Some(NextTokenChooserParameters {
                 temperature: 1.0,
@@ -241,7 +241,7 @@ impl Health for ShardedClient {
             top_n_tokens: 0,
             // Block 0 is reserved for health checks
             blocks: vec![0],
-            slots: (0..16).collect(),
+            slots: vec![0],
             cache_len: 0,
             adapter_id: None,
             chunk_len: None,
diff --git a/router/src/infer/mod.rs b/router/src/infer/mod.rs
index 866066438..6497d8578 100644
--- a/router/src/infer/mod.rs
+++ b/router/src/infer/mod.rs
@@ -33,6 +33,13 @@ pub trait Backend {
     ) -> Result<UnboundedReceiverStream<Result<InferStreamResponse, InferError>>, InferError>;
 
     async fn health(&self, current_health: bool) -> bool;
+
+    /// The state of the health on startup
+    /// Typically false, or true if the backend includes
+    /// a warmup phase.
+    fn start_health(&self) -> bool {
+        false
+    }
 }
 
 /// Inference struct
@@ -75,7 +82,7 @@ impl Infer {
         let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
 
         // Backend health
-        let backend_health = Arc::new(AtomicBool::new(false));
+        let backend_health = Arc::new(AtomicBool::new(backend.start_health()));
 
         Self {
             validation,
diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
index 2dcd1bf30..fbf1a597c 100644
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@@ -78,6 +78,7 @@ class RWConfig(PretrainedConfig):
         self.alibi = False
         self.rotary = True
         self.rope_theta = rope_theta
+        self.max_position_embeddings = 2048
 
         self.vocab_size = vocab_size
         # Backward compatibility with n_embed kwarg
diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index 07b7604d6..5d3769907 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -1304,6 +1304,7 @@ class FlashCausalLM(Model):
 
         self.num_layers = config.num_hidden_layers
         self.num_heads = config.num_attention_heads // self.process_group.size()
+        self.config = config
         # Validation is done in the model itself
         if num_kv_heads is None:
             num_kv_heads = getattr(config, "num_key_value_heads", None)
@@ -1594,7 +1595,10 @@ class FlashCausalLM(Model):
         if max_total_tokens is None:
             if get_support_chunking():
                 model_max_length = self.tokenizer.model_max_length
-                max_total_tokens = min(num_blocks * BLOCK_SIZE, model_max_length)
+                max_position_embeddings = self.config.max_position_embeddings
+                max_total_tokens = min(
+                    num_blocks * BLOCK_SIZE, model_max_length, max_position_embeddings
+                )
             else:
                 max_total_tokens = sum(batch.cache_lengths)