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LLM Efficiency Suite

Hardware‑aware software layer that cuts LLM inference energy by 70% without retraining.

Key features

  • ✔️ **70.7% energy savings** – measured on 100k realistic queries
  • ✔️ **Hardware suppression gate** – physically blocks GPU/TPU activation for routine queries
  • ✔️ **82.7% of queries** answered without invoking base LLM
  • ✔️ **Active learning** – 11 keywords promoted to C2 scope
  • ✔️ **Hardware‑agnostic** – works with any LLM (OpenAI, Anthropic, open source)
  • ✔️ **No retraining** – plug‑and‑play software layer
  • ✔️ **Patent‑protected** (US 64/006,312, European filing pending)

How it works

The Suite installs as a lightweight proxy between your application and the LLM API. It learns keyword patterns in real time, builds hierarchical scopes (C1–C4), and uses a predictive reconfiguration layer to detect bursts and trends. Routine queries are answered locally, saving both energy and latency. The **hardware suppression gate** ensures that the accelerator is not even powered up when the routine layer suffices, delivering true energy savings at the silicon level.

70.7%
energy saved
82.7%
queries without base LLM
11
keywords in C2 – active learning

Product readiness

The core model is **complete and validated at TRL5** with over 100,000 test queries. Final manufacturing steps include SDK packaging, deployment automation, and platform integration. The product is ready for pilot deployments and early‑access programmes.

✅ Algorithm finalised
✅ Patent filed
✅ Simulation & hardware validation passed
🔄 Final integration in progress

Expected general availability: Q3 2026.

Licensing & deployment

Available as:

  • 🔹 **SaaS subscription** – per‑token or per‑instance pricing
  • 🔹 **Enterprise licence** – on‑premises deployment for data centres
  • 🔹 **OEM integration** – for chip manufacturers and cloud providers

Contact us for a pilot or technical demo.

Request trial →