Edge Intelligence

Renia Micro LLM

A hyper-optimized, sub-100MB domain-specific language model designed to run completely offline on resource-constrained ARM, RISC-V, and microcontroller edge hardware.

Model Weight
Shrinker Dashboard

Manually compile Renia Micro LLM by adjusting weight bit-widths and sparsity ratios. Watch the binary scale contract and processing speed shoot up live.

Select Quantization Bits

Select Sparse Pruning Ratio

Compiler-Tuning --WeightPruning

Dynamic Sync

Final Binary Size0.90 GB

Compute Latency1.79 ms

Accuracy Retention98.9%

Quantized Weight Registers Matrix

> renia-micro --compile --target=ARM-M4

> Mapped attention weights... Final footprint verified at 0.90 GB

Renia Micro Edge Stack

We make complex language processing highly portable so it can operate without dynamic cloud dependencies.

Sub-100MB Binary Scale

Pruning redundant network parameters to squeeze files down to standard microcontroller scales, fitting inside local disk partitions easily.

Sub-5ms Execution Delays

Tuning matrix mathematical channels to complete calculations inside direct RAM addresses, creating instant response loops.

Local Quantized Compiling

Direct hardware compiler passes mapping model parameters directly onto ARM Cortex, Apple Silicon, and x86 chips.

Core Offline Implementations

Isolated IoT Smart Hubs

Embedding Renia Micro LLM into edge gateway enclosures inside remote wind turbine networks to run automatic system diagnostics without active WAN uplinks.

Direct Hardware Calibration

Integrating the micro model inside machinery logic interfaces to synthesize repair calibrations in response to sensor anomalies in real-time.

Unlock Edge Language Execution

Connect with our edge compilation lab to compress your models below 100MB or request direct hardware licenses.