6 Articles

LLM Inference Infrastructure

LLM inference optimization, hardware selection, serving platforms, and cost benchmarks. Reduce cost-per-token by 5–10x with the right infrastructure and serving stack.

All Guides

Technical26 min

LLM Inference Optimization

How to reduce cost-per-token by 5–10x using continuous batching, PagedAttention, quantization, and speculative decoding on production GPU infrastructure.

Read Guide

Decision22 min

Inference Hardware Selection

GPU selection for LLM inference — H100, L40S, A100, and the decision framework for matching hardware to model size, latency SLAs, and throughput requirements.

Read Guide

Technical24 min

Inference at Scale

Scaling LLM inference from prototype to production — load balancing, autoscaling, multi-region deployment, and the infrastructure patterns for millions of daily requests.

Read Guide

Technical20 min

Edge AI Inference

Deploying AI inference at the edge — hardware selection, model optimization for edge constraints, latency requirements, and management at scale.

Read Guide

Financial18 min

Inference Cost Benchmarks

Cost-per-token benchmarks across GPU hardware and serving frameworks — H100 vs. A100 vs. L40S, vLLM vs. TensorRT-LLM, and cloud vs. on-premises TCO.

Read Guide

Technical22 min

Model Serving Platforms

Comparison of LLM serving platforms — vLLM, TensorRT-LLM, TGI, Triton, and the selection criteria for each production inference use case.

Read Guide

Reference

Key Concepts

Continuous Batching

Dynamic batching that processes requests as they arrive — the primary technique for maximizing GPU utilization in LLM serving.

PagedAttention

vLLM's KV cache memory management system — eliminates fragmentation and enables 2–4x more concurrent requests on the same GPU.

Quantization

INT8, INT4, and FP8 quantization — reducing model memory footprint and increasing throughput with minimal quality degradation.

Cost per Token

The primary economic metric for LLM inference — how to measure, benchmark, and optimize cost-per-token across hardware and serving configurations.

Ready to Build Your AI Infrastructure?

Our certified engineers design and deploy enterprise AI infrastructure — from single GPU servers to 1,000+ GPU clusters.

Optimize Your Inference Infrastructure

Continue exploring

AI Inference Infrastructure Guides

LLM Inference Infrastructure

All Guides

LLM Inference Optimization

Inference Hardware Selection

Inference at Scale

Edge AI Inference

Inference Cost Benchmarks

Model Serving Platforms

Key Concepts

Continuous Batching

PagedAttention

Quantization

Cost per Token

Related Topics

Ready to Build Your AI Infrastructure?

Related resources

Related topics

Related solutions