Added VRAM release mechanism

gpu_server.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import os
 import json
+import time
 from contextlib import asynccontextmanager
 from typing import Dict, Any
 
@@ -23,11 +24,47 @@ logger = logging.getLogger("gpu-node")
 
 EMBED_MODEL_NAME = "nomic-ai/nomic-embed-text-v1.5"
 LLM_MODEL_PATH = os.getenv("LLM_MODEL_PATH", "/app/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf")
+LLM_IDLE_TIMEOUT = int(os.getenv("LLM_IDLE_TIMEOUT", "1800"))  
 TARGET_DIMENSIONS = 768
 
 state: Dict[str, Any] = {}
 gpu_semaphore = asyncio.Semaphore(1)
 
+
+def _load_llm() -> Llama:
+    logger.info(f"Loading LLM: {LLM_MODEL_PATH}")
+    return Llama(model_path=LLM_MODEL_PATH, n_gpu_layers=-1, n_ctx=8192, n_batch=512, verbose=False)
+
+def _unload_llm():
+    llm = state.pop("llm", None)
+    del llm
+    if cuda.is_available():
+        cuda.empty_cache()
+    logger.info("LLM unloaded due to inactivity")
+
+async def _inactivity_watcher():
+    while True:
+        await asyncio.sleep(60)
+        llm = state.get("llm")
+        last_used = state.get("llm_last_used")
+        if llm is not None and last_used is not None:
+            if time.monotonic() - last_used > LLM_IDLE_TIMEOUT:
+                async with gpu_semaphore:
+                    _unload_llm()
+
+def _touch_llm():
+    state["llm_last_used"] = time.monotonic()
+
+async def _ensure_llm() -> Llama:
+    llm = state.get("llm")
+    if llm is None:
+        if not os.path.exists(LLM_MODEL_PATH):
+            raise HTTPException(status_code=503, detail="LLM model file not found.")
+        loop = asyncio.get_event_loop()
+        state["llm"] = await loop.run_in_executor(None, _load_llm)
+    _touch_llm()
+    return state["llm"]
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     device = "cuda" if cuda.is_available() else "cpu"
@@ -45,24 +82,21 @@ async def lifespan(app: FastAPI):
         )
 
         if not os.path.exists(LLM_MODEL_PATH):
-            logger.error(f"LLM File not found at {LLM_MODEL_PATH}")
+            logger.warning(f"LLM file not found at {LLM_MODEL_PATH} — will load on first request")
         else:
-            logger.info(f"Loading LLM: {LLM_MODEL_PATH}")
-            state["llm"] = Llama(
-                model_path=LLM_MODEL_PATH,
-                n_gpu_layers=-1,
-                n_ctx=8192,
-                n_batch=512,
-                verbose=False
-            )
+            state["llm"] = _load_llm()
+            _touch_llm()
 
-        logger.info("--- GPU Node Ready ---")
+        logger.info(f"--- GPU Node Ready (LLM idle timeout: {LLM_IDLE_TIMEOUT}s) ---")
     except Exception as e:
         logger.error(f"Failed to load models: {e}")
         raise e
 
+    watcher = asyncio.create_task(_inactivity_watcher())
+
     yield
 
+    watcher.cancel()
     state.clear()
     if cuda.is_available():
         cuda.empty_cache()
@@ -235,9 +269,7 @@ async def chat_completions(request: Request):
 
     logger.info(f"Chat completion request: {len(messages)} messages, stream={stream}")
 
-    llm = state.get("llm")
-    if not llm:
-        raise HTTPException(status_code=503, detail="LLM not initialized or model file missing.")
+    llm = await _ensure_llm()
 
     loop = asyncio.get_event_loop()
     temperature = data.get("temperature", 0.7)
@@ -254,7 +286,6 @@ async def chat_completions(request: Request):
 
     try:
         if stream:
-            # For streaming, run inference in executor and stream results back
             def _infer_stream():
                 return llm.create_chat_completion(
                     messages=messages,