The KV Cache Bottleneck
Deploying LLMs in production is no longer about the model weights; it's entirely about KV cache memory management. We were initially serving our fine-tuned Llama-3 models using a naive FastAPI wrapper. Under concurrent load, the GPU memory would fragment instantly, causing OOM errors and crashing the pod.
Migrating to Triton + vLLM
We migrated our inference stack to Nvidia's Triton Inference Server utilizing the vLLM backend. The PagedAttention implementation in vLLM maps non-contiguous memory blocks to contiguous virtual memory, completely eliminating KV cache fragmentation.
# Launching the Triton Server with vLLM backend
docker run --gpus all -it --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:24.03-vllm-python-py3 tritonserver --model-repository=/modelsProduction Tuning
Here is the secret sauce for maxing out A100 utilization: make sure your gpu_memory_utilization is tuned perfectly in your config.pbtxt. We set ours to 0.95. If you leave it at the default, you are leaving massive batching throughput on the table. This single configuration change allowed us to increase our maximum concurrent batch size from 16 to 128, hitting sustained throughputs of 5,000 tokens/sec across the cluster.