diff --git a/src/llm_vm/onsite_llm.py b/src/llm_vm/onsite_llm.py
index f0154b1c..a70f5a08 100644
--- a/src/llm_vm/onsite_llm.py
+++ b/src/llm_vm/onsite_llm.py
@@ -27,6 +27,7 @@
 from peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training
 from trl import SFTTrainer
 from sentence_transformers import SentenceTransformer
+from vllm import LLM, SamplingParams
 
 
 
@@ -86,7 +87,7 @@ def __getitem__(self, idx):
         return self.dataset[idx]
 
 class BaseOnsiteLLM(ABC):
-    def __init__(self,model_uri=None, tokenizer_kw_args={}, model_kw_args={}):
+    def __init__(self,model_uri=None, vllm_support=True, tokenizer_kw_args={}, model_kw_args={}):
         if model_uri != None :
             self.model_uri= model_uri
         if model_uri is None and self.model_uri is None:
@@ -94,6 +95,7 @@ def __init__(self,model_uri=None, tokenizer_kw_args={}, model_kw_args={}):
         self.model_name : str = self.model_uri.split('/')[-1] # our default for deriving model name
         self.model=self.model_loader(**model_kw_args)
         self.tokenizer=self.tokenizer_loader(**tokenizer_kw_args)
+        self.vllm_support=vllm_support
 
         # Move the model to the specified device(s)
         if isinstance(device, list):
@@ -145,6 +147,12 @@ def generate(self,prompt,max_length=100, tokenizer_kwargs={}, generation_kwargs=
            I think it takes about a week for the apple to grow.
         """
 
+        if generation_kwargs['num_return_sequences']>1 and self.vllm_support:
+            print("doing parallel sampling using vllm")
+            sampling_params = SamplingParams(n=generation_kwargs['num_return_sequences'], max_tokens=max_length)
+            llm = LLM(model=self.model_uri)
+            outputs = llm.generate(prompt, sampling_params)
+            return [outputs[0].outputs[i].text for i in range(generation_kwargs['num_return_sequences'])]
 
         if isinstance(device, list):
             # If multiple GPUs are available, use first one
@@ -442,6 +450,7 @@ class SmallLocalNeo(BaseOnsiteLLM):
         generate: Generates a response from a given prompt with the loaded LLM and tokenizer
     """
     model_uri="EleutherAI/gpt-neo-1.3B"
+    vllm_support = False
 
     def model_loader(self):
         return GPTNeoForCausalLM.from_pretrained(self.model_uri)
@@ -680,6 +689,7 @@ class SmallLocalFlanT5(BaseOnsiteLLM):
     """
 
     model_uri="google/flan-t5-small"
+    vllm_support = False
     def model_loader(self):
         return AutoModelForSeq2SeqLM.from_pretrained(self.model_uri)
     def tokenizer_loader(self):
@@ -704,6 +714,7 @@ class SmallLocalBERT(BaseOnsiteLLM):
     """
 
     model_uri = "bert-base-cased"
+    vllm_support = False
     def model_loader(self):
         return AutoModelForMaskedLM.from_pretrained(self.model_uri)
     def tokenizer_loader(self):