Concepts of LLM Serving

2026-03-29 · Source: Daily Dose of Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

This article, part 14 of an LLMOps series, provides an overview of LLM serving fundamentals, contrasting API-based access with self-hosted inference. It details the unique challenges of serving large language models, such as high VRAM consumption, sequential request handling in naive setups, and complex scaling, which differ significantly from traditional ML model deployments. The discussion focuses on self-hosted inference, exploring deployment topologies including on-premises, cloud, and hybrid setups. On-premises deployments offer data security, compliance, and predictable costs for regulated industries and high-volume workloads, despite high upfront costs and operational complexity. Cloud deployments provide flexibility, access to new GPUs, and horizontal scaling but incur variable costs and data egress concerns. Hybrid approaches combine on-prem baseline capacity with cloud overflow for cost efficiency and elasticity.

Key takeaway

For MLOps Engineers planning LLM deployments, carefully evaluate your data privacy requirements, cost predictability needs, and expected traffic variability. If data security is paramount or you anticipate high, steady traffic, prioritize an on-premises or hybrid setup. For early-stage projects or bursty workloads, cloud deployments offer necessary flexibility and access to cutting-edge hardware, but monitor costs closely.

Key insights

LLM serving requires managing significant compute and memory resources, differing from traditional ML deployment.

Principles

• Inference has prefill (compute-bound) and decode (memory-bound) phases.
• Self-hosting LLMs offers control over data, cost, and configuration.
• Deployment topology impacts security, cost, and operational overhead.

Method

LLM serving involves choosing between API providers or self-hosting, then selecting a deployment topology (on-prem, cloud, or hybrid) based on data security, cost, and scalability needs.

In practice

• Use continuous batching to maximize GPU utilization.
• Implement KV caching to reduce redundant computation.
• Consider hybrid deployments for cost and elasticity.

Topics

• LLM Serving
• API-Based Inference
• Self-Hosted LLMs
• Deployment Topologies
• On-Premises Deployment

Best for: MLOps Engineer, Machine Learning Engineer, AI Engineer

Related on AIssential

• How to Deploy Your LLM in the Cloud — Self-hosting LLMs offers greater control and cost optimization compared to serverless solutions for production deployments.
• Security in an LLM Observability Platform: Controls, Tradeoffs, and What Is Still Open — LLM observability platforms require integrated security controls for sensitive prompt and completion data.
• On-Prem vs. Proxy Solutions for Secure LLM Usage: A Practical Guide for Enterprises — The deployment architecture for Large Language Models (LLMs) has become the defining factor for enterprise AI security, compliance, and sustainability, shifting focus from model capability to governance.
• I Deployed Local LLMs in Production for a Year. Part 2: The Operational Playbook — Optimizing local LLM deployments requires careful configuration of context, KV cache, and parallelism to balance memory, throughput, and latency.
• Inference Optimization in LLMs: A Systems View — Inference optimization for LLMs is a layered systems problem, not a single technique, where memory bandwidth is the primary constraint.
• Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving — Optimizing CPU LLM serving requires separate execution plans for prefill and decode phases due to their distinct computational and memory demands.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Daily Dose of Data Science.