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.
Summary
A pattern of running ML/LLM models on local hardware against data stored in or pulled from S3, avoiding cloud-based inference APIs.
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.
- "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.
solvesHigh Cloud Inference Cost, Egress Cost — eliminates per-token and egress chargesscoped_toLLM-Assisted Data Systems, S3
Definition
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.
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.
On-premise embedding generation, local metadata extraction from S3-stored documents, edge inference for IoT data stored in S3.
Connections 4
Outbound 4
scoped_to2Resources 3
Official vLLM documentation for loading and serving LLM models directly from S3 using the Run:ai Model Streamer, enabling S3-backed local inference.
Canonical repository for llama.cpp, the most widely used C/C++ LLM inference engine for running quantized models on commodity hardware.
NVIDIA technical blog demonstrating how streaming models from S3 reduces cold-start latency and infrastructure costs.