Distributed GPU-accelerated inference gateway for custom LLMs with real-time semantic caching.
Challenge
Agentic systems can create bursty, repeated model calls. The useful layer is not just faster GPUs; it is a gateway that can cache, route, evaluate, and explain inference decisions.
Approach
- Separated the Python orchestration layer from Rust hot paths for request normalization, cache checks, and streaming response control.
- Used semantic caching to reuse high-confidence responses while keeping cache misses observable for evaluation.
- Treated every model call as an auditable event with latency, cost, prompt family, and downstream workflow context.
Outcome
- Reduced redundant inference work in repeatable agent flows.
- Created a stronger operational boundary between prototypes and production AI services.
- Made governance measurable through logs, routing policy, and failure-mode analysis.