Architecture

Local Inference Stack

A pattern of running ML/LLM models on local hardware against data stored in or pulled from S3, avoiding cloud-based inference APIs.

4 connections 3 resources 1 post

Summary

What it is

A pattern of running ML/LLM models on local hardware against data stored in or pulled from S3, avoiding cloud-based inference APIs.

Where it fits

This is the cost optimization pattern for LLM workloads over S3 data. When the volume of data to process is large enough, local inference (on-premise GPUs or edge devices) is orders of magnitude cheaper than per-token cloud API pricing.

Misconceptions / Traps

"Local" does not mean "free." GPUs, power, cooling, and operational overhead have real costs. The break-even point depends on volume and model size.
Model quality may differ. Smaller local models (distilled, quantized) trade accuracy for cost. Evaluate whether the quality loss is acceptable for your use case.

Key Connections

solves High Cloud Inference Cost, Egress Cost — eliminates per-token and egress charges
scoped_to LLM-Assisted Data Systems, S3

Definition

What it is

A pattern of running ML/LLM models on local hardware (on-premise or edge) against data stored in or pulled from S3, avoiding cloud-based inference APIs entirely.

Why it exists

Cloud inference costs scale linearly with volume. For organizations processing large amounts of S3-stored data, running models locally on owned hardware can be orders of magnitude cheaper — and eliminates egress charges.

Primary use cases

On-premise embedding generation, local metadata extraction from S3-stored documents, edge inference for IoT data stored in S3.

Recent developments

Latest signals

Local LLM inference is now the preferred default for many use cases. Per the 2026 industry framing, running large language models locally on consumer hardware is not just feasible but the preferred default for a growing number of developers and organizations — full data privacy, offline functionality, significant cost savings. Per Programming Helper — Local LLM Inference 2026.
Three-tier stack: Ollama (single-dev) → vLLM (multi-user serving) → llama.cpp (foundation). Ollama wraps llama.cpp with a Docker-like CLI for simplicity; vLLM is the multi-user serving engine; llama.cpp is the foundational C++ inference engine both build on. Choose Ollama for single-developer interactive use, vLLM for 10-100 concurrent users, llama.cpp when custom compilation + hardware-specific optimization is needed. Per DecodesFuture — llama.cpp vs Ollama vs vLLM 2026 Benchmarks.
vLLM benchmarked at 793 tok/s vs Ollama's 41 tok/s on the same hardware. 2026 benchmark numbers: vLLM achieves 793 tokens/second under concurrent request load vs Ollama's 41 tokens/second on the same hardware. The order-of-magnitude gap is the structural reason teams graduate from Ollama → vLLM as concurrency scales. Per DecodesFuture — Benchmarks.
Security: bind to localhost + reverse proxy + TLS + auth. Ollama binds to localhost:11434 by default (correct). DO NOT bind to 0.0.0.0 to expose externally — instead, place NGINX or Caddy as a reverse proxy with TLS + basic auth + rate limiting. Per SitePoint — Definitive Guide to Local LLMs 2026.
Ecosystem components: model hosts + inference engines + serving + frameworks + eval. Full stack: Hugging Face / Ollama (model hosts) + llama.cpp / vLLM (inference engines) + Ollama server / text-generation-webui (serving) + LangChain / LlamaIndex (frameworks) + lm-evaluation-harness (eval pipelines). Per Daily.dev — Running LLMs Locally with Ollama, llama.cpp.
Enterprise local-LLM deployment guide published 2026. SitePoint's 2026 definitive guide covers vLLM + GPUs + production deployment for enterprise local-LLM stacks — closes the "is local-first really enterprise-grade?" question. Per SitePoint — 2026 Definitive Guide to Running Local LLMs in Production.