Zero Downtime LLM Inference: The Waterfall Approach
It's 2 AM. Your AI agent is in the middle of a critical code generation task. OpenAI returns a 429 — rate limited. Your workflow stops. You wait. You retry. You wait longer. Productivity: zero.
This doesn't happen with HyperNexus.
The Three-Tier Waterfall
HyperNexus's inference client catches 429s (rate limits) and 5xx (server errors) automatically, cascading the exact payload down a prioritized chain:
NVIDIA NIM / Primary APIs
Google, Anthropic, OpenAI, DeepSeek, GitHub Copilot — your preferred providers, tried first.
OpenRouter (Aggregator Fallback)
If your primary provider is down or rate-limited, the exact same request routes through OpenRouter's multi-provider network.
Local LM Studio / Ollama
The ultimate fallback: local inference on your own hardware. Zero network dependency. Works offline.
How It Works
The waterfall is transparent to your workflow. You don't see it. You don't manage it. It just works:
Automatic detection: The client monitors response codes. 429, 500, 502, 503 — all trigger cascade. No manual retry logic needed.
Zero config: Set your provider priority once. The waterfall handles the rest. If tier 1 is healthy, tier 2 and 3 are never touched.
Same payload: The exact same prompt, system message, and parameters cascade through all tiers. No reformatting. No translation. Identical output structure.
The Provider Catalog
HyperNexus supports a growing catalog of providers:
Cloud: Google (Gemini), Anthropic (Claude), OpenAI (GPT), DeepSeek, GitHub Copilot
Aggregators: OpenRouter (100+ models through one API)
Local: LM Studio, Ollama (any GGUF model)
Why This Matters
Uptime is non-negotiable. When your AI agent is your primary development tool, downtime means zero productivity. The waterfall approach means your agent is always available, always working, always productive.
Stop waiting for rate limits to clear. Start cascading automatically.