diff --git a/src/llmperf/common.py b/src/llmperf/common.py
index 3efefa1..7af546b 100644
--- a/src/llmperf/common.py
+++ b/src/llmperf/common.py
@@ -5,10 +5,11 @@
 )
 from llmperf.ray_clients.sagemaker_client import SageMakerClient
 from llmperf.ray_clients.vertexai_client import VertexAIClient
+from llmperf.ray_clients.predibase_client import PrediBaseClient
 from llmperf.ray_llm_client import LLMClient
 
 
-SUPPORTED_APIS = ["openai", "anthropic", "litellm"]
+SUPPORTED_APIS = ["openai", "anthropic", "litellm", "predibase"]
 
 
 def construct_clients(llm_api: str, num_clients: int) -> List[LLMClient]:
@@ -28,6 +29,8 @@ def construct_clients(llm_api: str, num_clients: int) -> List[LLMClient]:
         clients = [SageMakerClient.remote() for _ in range(num_clients)]
     elif llm_api == "vertexai":
         clients = [VertexAIClient.remote() for _ in range(num_clients)]
+    elif llm_api == "predibase":
+        clients = [PrediBaseClient.remote() for _ in range(num_clients)]
     elif llm_api in SUPPORTED_APIS:
         clients = [LiteLLMClient.remote() for _ in range(num_clients)]
     else:
diff --git a/src/llmperf/ray_clients/predibase_client.py b/src/llmperf/ray_clients/predibase_client.py
new file mode 100644
index 0000000..08212f8
--- /dev/null
+++ b/src/llmperf/ray_clients/predibase_client.py
@@ -0,0 +1,127 @@
+import io
+import json
+import os
+import time
+from typing import Any, Dict
+
+import ray
+import requests
+
+from llmperf.ray_llm_client import LLMClient
+from llmperf.models import RequestConfig
+from llmperf import common_metrics
+
+
+
+@ray.remote
+class PrediBaseClient(LLMClient):
+
+    def llm_request(self, request_config: RequestConfig) -> Dict[str, Any]:
+        prompt = request_config.prompt        
+        prompt, prompt_len = prompt
+
+        if not request_config.sampling_params:             
+            raise ValueError("Set sampling_params to set the parameters in request body.")
+        else:
+            request_config.sampling_params['max_new_tokens'] = request_config.sampling_params.pop('max_tokens')        
+        
+        body = {
+            "inputs": prompt,
+            "parameters": request_config.sampling_params        
+        }
+
+        time_to_next_token = []
+        tokens_received = 0
+        ttft = 0
+        error_response_code = -1
+        generated_text = ""
+        error_msg = ""
+        output_throughput = 0
+        total_request_time = 0
+
+        metrics = {}
+
+        metrics[common_metrics.ERROR_CODE] = None
+        metrics[common_metrics.ERROR_MSG] = ""
+
+        start_time = time.monotonic()
+        most_recent_received_token_time = time.monotonic()
+
+        address = os.environ.get("PREDIBASE_API_BASE")
+        key = os.environ.get("PREDIBASE_API_KEY")
+        
+
+        if not address:
+            raise ValueError("the environment variable PREDIBASE_API_BASE must be set.")
+                
+        headers = {'Content-Type': 'application/json'}
+        if not key:
+            print(f"Warning: PREDIBASE_API_KEY is not set.")            
+        else:
+            headers["Authorization"] =  f"Bearer {key}" 
+        
+        if not address:
+            raise ValueError("No host provided.")
+        if not address.endswith("/"):
+            address = address + "/"
+        address += "generate_stream"
+
+        try:
+            with requests.post(
+                address,
+                json=body,
+                stream=True,
+                timeout=180,
+                headers=headers,
+            ) as response:                
+                if response.status_code != 200:
+                    error_msg = response.text
+                    error_response_code = response.status_code
+                    response.raise_for_status()
+
+                for chunk in response.iter_lines(chunk_size=None):
+                    chunk = chunk.strip()
+
+                    if not chunk:
+                        continue
+                    stem = "data:"
+                    chunk = chunk[len(stem) :]
+                    if chunk == b"[DONE]":
+                        continue
+                    tokens_received += 1                    
+                    data = json.loads(chunk)                    
+                    if "error" in data:
+                        error_msg = data["error"]
+                        raise RuntimeError(error_msg)
+                        
+                    delta = data["token"]
+                    if delta.get("text", None):
+                        if not ttft:
+                            ttft = time.monotonic() - start_time
+                            time_to_next_token.append(ttft)
+                        else:
+                            time_to_next_token.append(
+                                time.monotonic() - most_recent_received_token_time
+                            )
+                        most_recent_received_token_time = time.monotonic()
+                        generated_text += delta["text"]
+
+            total_request_time = time.monotonic() - start_time
+            output_throughput = tokens_received / total_request_time
+
+        except Exception as e:
+            metrics[common_metrics.ERROR_MSG] = error_msg
+            metrics[common_metrics.ERROR_CODE] = error_response_code
+            print(f"Warning Or Error: {e}")
+            print(error_response_code)
+
+        metrics[common_metrics.INTER_TOKEN_LAT] = sum(time_to_next_token) #This should be same as metrics[common_metrics.E2E_LAT]. Leave it here for now
+        metrics[common_metrics.TTFT] = ttft
+        metrics[common_metrics.E2E_LAT] = total_request_time
+        metrics[common_metrics.REQ_OUTPUT_THROUGHPUT] = output_throughput
+        metrics[common_metrics.NUM_TOTAL_TOKENS] = tokens_received + prompt_len
+        metrics[common_metrics.NUM_OUTPUT_TOKENS] = tokens_received
+        metrics[common_metrics.NUM_INPUT_TOKENS] = prompt_len
+
+        return metrics, generated_text, request_config
+