Has anyone moved from single-request testing to async/threaded high concurrency setups?? That painful drop or massive p99 latency spike you’re seeing isnt a bug in your Python or go code - its a mismatch on the backend inference server. This is where simple scaling just breaks down.
The core issue: When you’re using an inference server with static batching, the moment multiple requests hit the LLM at once, you run into two resource-wasting problems -
Tail latency hostage - The whole batch gets locked until the longest sequence finishes. A 5 token answer sits there waiting for a 500 token verbose response. This creates high p99 latency and frustrates users who just wanted a quick answer. 1.
Wasted GPU cycles - The kv cache sits idle... as soon as a short request completes…
Has anyone moved from single-request testing to async/threaded high concurrency setups?? That painful drop or massive p99 latency spike you’re seeing isnt a bug in your Python or go code - its a mismatch on the backend inference server. This is where simple scaling just breaks down.
The core issue: When you’re using an inference server with static batching, the moment multiple requests hit the LLM at once, you run into two resource-wasting problems -
Tail latency hostage - The whole batch gets locked until the longest sequence finishes. A 5 token answer sits there waiting for a 500 token verbose response. This creates high p99 latency and frustrates users who just wanted a quick answer. 1.
Wasted GPU cycles - The kv cache sits idle... as soon as a short request completes, its allocated key/value cache memory gets freed but just sits there doing nothing. The GPU’s parallel resources are now waiting for the rest of the batch to catch up, leading to GPU underutilization.
This performance hit happens whether you’re running local engines like llama.cpp (which often handles requests one by one) or hitting public APIs like deepinfra or azure under heavy load. The key issue is how the single loaded model manages resources.
The client side trap: Server side batching is the main culprit but your client implementation can make it worse. A lot of people try to fix slow sequential loops by firing tons of requests at once - like 100+ simultaneous requests via basic threading. This leads to:
Requests piling up causing long wait times and potential timeouts as the server’s queue fills
Context switching overhead. Even modern schedulers struggle with a flood of simultaneous connections, which reduces efficiency
The fix here is managed concurrency. Use async patterns with semaphore-based limits like python’s asyncio.semaphore to control how many requests run at the same time - maybe 5-10 simultaneous calls to match what the API can realistically handle. This prevents bottlenecks before they even hit the inference server.
Better system approach - continuous batching + pagedAttention: The real solution isnt “more threads” but better scheduler logic and memory management on the server side. The current standard is continuous batching (or flight batching) combined with pagedAttention. Instead of waiting for batch boundaries, continuous batching works at the token level -
As soon as a sequence finishes, its kv cache memory gets released immediately
pagedAttention manages memory non-contiguously (like virtual memory paging), letting new requests immediately grab available memory slots
This dynamic approach maximizes GPU usage and eliminates tail latency spikes while drastically improving throughput. Tools that implement this include vLLM, Hugging Face TGI, and TensorRT-LLM.