Hyper-Inference Router: AI Routing on MI300X

Most AI products pick one model and use it for every request, regardless of what that request actually needs. That means either overpaying a frontier model to answer a two-word greeting, or underpowering a request that genuinely needed deep reasoning to produce a good answer. Hyper-Inference Router fixes this by classifying every incoming prompt and routing it to the model actually suited to the job: - Casual/simple queries -> a fast, cheap serverless model - Reasoning and creative tasks (proposals, strategy, marketing copy, storytelling) -> Gemma 4 E4B (Google DeepMind), running on a dedicated AMD Instinct MI300X deployment via Fireworks AI - Code-related queries -> a code-specialized model Classification itself is a zero-cost weighted keyword match, so the routing layer doesn't add its own LLM-call overhead on every request. This isn't a hackathon-only prototype. The routing pattern was built for and is used inside Kronos AI (kronos.devxhouse.com), a live SaaS platform for freelancers and agencies that generates job proposals, social content, and business documents on demand for paying users. This submission is a clean, standalone extraction of that exact routing logic, rebuilt without any of Kronos's business logic or proprietary code, specifically so it can be run, tested, and verified in isolation. Every response includes real, live numbers: actual model used, latency in milliseconds, token count, cost in USD, and a live cost comparison against equivalent GPT-4o pricing — computed from the same call, not asserted. Gemma 4 E4B (8B total / ~4.5B effective parameters) is a compact multimodal model, chosen specifically because it's cheap enough to run as a dedicated deployment while still giving a real quality step up over the cheapest serverless models for tasks that need actual reasoning. A dedicated deployment gives predictable latency with no per-request rate limits once warm. Try it live: submit any prompt at the demo URL and watch it route in real.