LLM Inference Serving Optimizer

Deploying a large language model in development is straightforward. Serving it reliably at production scale — with acceptable latency, high throughput, and controlled cost — is an entirely different engineering challenge. The LLM Inference Serving Optimizer helps ML engineers and platform teams design, configure, and tune their inference serving stack to meet real production requirements.

This assistant focuses exclusively on the inference serving layer: the software, hardware, and configuration decisions that determine how efficiently your deployed model handles requests. It covers the leading serving frameworks — vLLM, TensorRT-LLM, TGI (Text Generation Inference), Triton Inference Server, and llama.cpp — explaining the trade-offs between them in terms of throughput, latency, hardware compatibility, and operational complexity.

The assistant works through the key optimization levers available to inference engineers. Continuous batching and PagedAttention (as implemented in vLLM) dramatically increase GPU utilization compared to static batching — the assistant explains how these mechanisms work and how to configure them for your traffic patterns. Quantization strategies (INT8, INT4, GPTQ, AWQ, FP8) reduce memory footprint and increase throughput at the cost of some precision, and the assistant helps you evaluate that trade-off for your specific model and quality requirements.

For multi-GPU and multi-node inference, it covers tensor parallelism degree selection, pipeline parallelism for very large models, and the networking requirements that enable efficient distributed inference. It also addresses KV cache sizing, prefill vs. decode phase optimization, speculative decoding, and prompt caching for workloads with shared prefixes.

The assistant helps you build a performance model: given your model size, hardware, and traffic SLA, what throughput can you achieve, at what latency percentile, and at what cost per million tokens? This output is directly useful for capacity planning, cost forecasting, and SLA commitment decisions.

Ideal users include ML engineers preparing production LLM deployments, platform teams benchmarking inference infrastructure, and engineering leads evaluating self-hosted vs. API-based inference for cost and control.