diff --git a/llm_server/llm/vllm/tokenize.py b/llm_server/llm/vllm/tokenize.py
index 6bb9343..a698fd6 100644
--- a/llm_server/llm/vllm/tokenize.py
+++ b/llm_server/llm/vllm/tokenize.py
@@ -5,14 +5,22 @@ from llm_server import opts
 
 
 def tokenize(prompt: str) -> int:
-    tokenizer = tiktoken.get_encoding("cl100k_base")
     if not prompt:
         # The tokenizers have issues when the prompt is None.
         return 0
-    try:
-        r = requests.post(f'{opts.backend_url}/tokenize', json={'input': prompt}, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
-        j = r.json()
-        return j['length']
-    except Exception as e:
-        print(f'Failed to tokenize using VLLM -', f'{e.__class__.__name__}: {e}')
-        return len(tokenizer.encode(prompt)) + 10
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+
+    # First we tokenize it locally to determine if it's worth sending it to the backend.
+    initial_estimate = len(tokenizer.encode(prompt))
+    if initial_estimate <= opts.context_size + 200:
+        try:
+            r = requests.post(f'{opts.backend_url}/tokenize', json={'input': prompt}, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
+            j = r.json()
+            return j['length']
+        except Exception as e:
+            print(f'Failed to tokenize using VLLM -', f'{e.__class__.__name__}: {e}')
+            return len(tokenizer.encode(prompt)) + 10
+    else:
+        # If the result was greater than our context size, return the estimate.
+        # We won't be sending it through the backend so it does't need to be accurage.
+        return initial_estimate
diff --git a/llm_server/routes/v1/generate_stats.py b/llm_server/routes/v1/generate_stats.py
index 5bf026b..e144099 100644
--- a/llm_server/routes/v1/generate_stats.py
+++ b/llm_server/routes/v1/generate_stats.py
@@ -89,7 +89,6 @@ def generate_stats(regen: bool = False):
             'average_generation_elapsed_sec': int(average_generation_time),
             # 'estimated_avg_tps': estimated_avg_tps,
             'tokens_generated': sum_column('prompts', 'response_tokens') if opts.show_total_output_tokens else None,
-            'nvidia': netdata_stats
         },
         'online': online,
         'endpoints': {
@@ -115,6 +114,7 @@ def generate_stats(regen: bool = False):
             'anthropicKeys': '∞',
         },
         'backend_info': redis.get_dict('backend_info') if opts.show_backend_info else None,
+        'nvidia': netdata_stats
     }
     result = deep_sort(output)