Concurrent Request Handling
Handle thousands of simultaneous LLM requests efficiently. Learn connection pooling, queue management, and auto-scaling strategies.
Async Request Handler
import asyncio
from concurrent.futures import ThreadPoolExecutor
class ConcurrentLLMHandler:
def __init__(self, max_concurrent=100):
self.semaphore = asyncio.Semaphore(max_concurrent)
self.executor = ThreadPoolExecutor(max_workers=50)
async def handle_request(self, request):
async with self.semaphore:
# Rate limiting per model
await self.rate_limiter.acquire(request.model)
try:
response = await self.process_with_retry(request)
return response
except Exception as e:
return await self.fallback_handler(request, e)
async def batch_process(self, requests):
tasks = [self.handle_request(req) for req in requests]
return await asyncio.gather(*tasks, return_exceptions=True)Concurrent Requests
10,000+
Simultaneous handling
Queue Time
<10ms
Average wait time
Success Rate
99.95%
With auto-retry
ParrotRouter handles connection pooling and concurrent request management automatically, scaling to handle your peak loads.
References
- [1] arXiv. "Efficient LLM Inference" (2024)
- [2] Hugging Face. "GPU Inference Optimization" (2024)
- [3] NVIDIA. "LLM Inference Optimization" (2024)