serving-llms-vllm

Deploy an OpenAI-compatible LLM serving API using vLLM in Kubernetes by applying distributed serving configurations to containerized environments. This approach exposes scalable, high-throughput inference endpoints with integrated metrics tracking for production workloads.

Fit large quantized models on limited hardware during LLM serving by applying AWQ, GPTQ, or FP8 post-training quantization methods. These techniques reduce memory footprints while maintaining efficient batching for high-throughput inference pipelines.

Yes, you can use vLLM for offline batch inference on large prompt sets. It processes extensive prompt collections efficiently using chunked prefill and prefix caching, complementing its core production API deployment capabilities.

Yes, vLLM supports tensor parallelism for distributed serving configurations. This partitions model execution across multiple GPUs, enabling high-throughput LLM serving and scaling workloads for demanding production API environments.

Prefix caching and chunked prefill improve high-throughput LLM serving by optimizing prompt processing. Prefix caching stores reused token contexts, while chunked prefill breaks down long inputs to enhance inference pipeline efficiency.

Use Docker for single-node vLLM production API deployment and Kubernetes for distributed serving configurations. Kubernetes scales workloads across clusters with metrics exposure, while Docker offers straightforward containerized environments for smaller setups.