Scaling production LLM APIs from a proof-of-concept to serving thousands of concurrent requests reveals immediate throughput ceilings: static batching starves GPU compute cores, while unmanaged KV caches cause out-of-memory crashes.
On NovaPod, our production inference templates combine continuous batching, chunked prefill, and PagedAttention to eliminate memory fragmentation entirely, delivering up to 4x higher token throughput compared to naive model servers.
On NVIDIA H200 SXM5 instances (141 GB HBM3e at 4.8 TB/s bandwidth), a single GPU can comfortably serve Llama 3 70B in FP8 precision while retaining sufficient memory headroom for 128k active context tokens.
Furthermore, all NovaPod serving nodes expose standard OpenAI-compatible endpoints with Server-Sent Events (SSE) streaming, making them instantaneous drop-in replacements for downstream applications.
python3 -m vllm.entrypoints.openai.api_server \
--model meta-llama/Meta-Llama-3-70B-Instruct \
--tensor-parallel-size 4 \
--gpu-memory-utilization 0.95 \
--enable-chunked-prefill \
--port 8000