ai-llm-inference

Optimize LLM inference throughput using vLLM with continuous batching, quantization techniques (FP8/FP4/INT8/INT4), and KV cache optimization. These methods reduce latency and increase requests-per-second on GPU or CPU infrastructure while maintaining model accuracy.

Quantization—converting models to FP8, FP4, INT8, or INT4 formats—cuts inference costs significantly with minimal accuracy loss. Combined with vLLM's continuous batching, it enables cost-efficient serving at scale.

Yes. Multi-model deployment with quantization, KV cache optimization, and continuous batching allows you to serve multiple models concurrently on shared GPUs, maximizing hardware utilization and reducing per-request cost.

Quantization reduces model size and memory bandwidth requirements, accelerating inference with lower hardware costs. FP8 and FP4 formats achieve 2–4x speedups with minimal accuracy degradation when properly calibrated.

vLLM supports GPU-accelerated inference for high throughput and CPU serving for lower-scale or cost-constrained deployments. GPU is preferred for production; CPU suits edge or resource-limited environments.

Production-ready inference requires configuration templates, decision rules, and operational checklists covering model quantization, batching strategy, hardware allocation, failover, and monitoring to ensure reliable, high-throughput, cost-efficient serving.