Hyperfusion Resources

Technical guides and deep dives covering GPU infrastructure, inference endpoints, data residency requirements, and model deployment for production AI workloads.

Blog Image

Deploying Qwen 3 on an OpenAI-compatible endpoint: a practical walkthrough

Deploying Qwen 3 on an OpenAI-compatible endpoint: a practical walkthroughHyperfusion Published on: 26/02/2026

This guide covers how to run Qwen 3 behind an endpoint that speaks the same API format as OpenAI, so that your existing application code continues to work with minimal modification. We will go from model selection through to a production inference endpoint on H100 GPUs, with code you can copy directly into your project.

Qwen3DeployIAOpenAI
Read More

How one AI platform cut inference costs by 40% by moving beyond OpenAI

How one AI platform cut inference costs by 40% by moving beyond OpenAIby: HyperfusionPublished on: 06/03/2026

Learn how a SaaS AI platform reduced inference costs by 40% by testing open-weight models and migrating part of its OpenAI workload to dedicated LLM infrastructure.

GuidesGPUPricing
Read more

The hidden costs behind GPU hourly pricing

The hidden costs behind GPU hourly pricingby: HyperfusionPublished on: 02/03/2026

Data sovereignty for AI workloads is becoming a hard requirement in the GCC, not just a preference. This post covers what the requirements actually are, why running inference on US or European hyperscalers does not fully solve the problem, and how regional GPU infrastructure changes the calculus.

GPUPricing
Read more
Hyperfusion.io