How to Deploy Your LLM in the Cloud

2026-02-23 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Serving Large Language Models (LLMs) in production primarily presents an infrastructure challenge, focusing on latency, throughput, and reliability, which are influenced by GPU selection, memory, batching, and the serving runtime. While serverless LLM solutions offer ease of setup and operational simplicity for spiky or low-volume traffic, they can lead to less control and unpredictable costs at scale due to variable prompt lengths, concurrency, and cold starts. Self-hosting LLMs, conversely, provides greater control over model versions, custom adapters, and data boundaries. It also enables direct ownership of performance and cost optimization through specific GPU and weight format choices (bf16/fp16/fp8/fp4/int4), along with fine-tuning batching and runtime configurations to meet speed and quality targets efficiently.

Key takeaway

For MLOps Engineers evaluating LLM deployment strategies, self-hosting provides critical control over model versions, data, and performance tuning, which is essential for predictable costs and optimized speed at scale. You should consider dedicated GPU solutions with inference engines like vLLM to fine-tune your serving stack, especially for consistent or high-volume workloads, to avoid the variable costs of serverless options.

Key insights

Self-hosting LLMs offers greater control and cost optimization compared to serverless solutions for production deployments.

Principles

• Infrastructure dictates LLM performance.
• Control improves cost predictability.
• GPU choice impacts speed and cost.

Method

Deploy an LLM on a dedicated GPU using vLLM for high throughput, leveraging platforms like RunPod for clear pricing and testing with tools such as AnythingLLM.

In practice

• Choose GPU based on model size.
• Select weight format (e.g., fp8, int4).
• Tune batching for workload type.

Topics

• LLM Deployment
• LLM Serving Infrastructure
• GPU Optimization
• vLLM Inference Engine
• Serverless vs. Self-hosting

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

Related on AIssential

• Concepts of LLM Serving — LLM serving requires managing significant compute and memory resources, differing from traditional ML deployment.
• Self-Hosting Your First LLM — This guide presents a practical playbook for self-hosting production-grade Large Language Models (LLMs) on a single machine, primarily for agent-oriented workloads, addressing concerns like exploding…
• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.
• Deploying Disaggregated LLM Inference Workloads on Kubernetes — Disaggregated LLM inference optimizes resource use and scaling by separating prefill, decode, and routing stages.
• vLLM vs Triton vs TGI: Choosing the Right LLM Serving Framework — Efficient LLM inference requires optimizing KV cache management and batching for high throughput and low latency.
• LLM‑D Explained: Building Next‑Gen AI with LLMs, RAG & Kubernetes — LLM-D optimizes LLM inference by intelligently routing requests and disaggregating prefill/decode phases on Kubernetes.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.