Frequently Asked Questions

No. ISIRO Runtime does not use quantization. You run the same model at the same precision in a smaller footprint, on a more memory-efficient execution path, with bit-exact weights. It does not approximate weights, retrain the model, or require calibration. Quantization can work for some workloads, but it changes the model’s numerical representation, often with a quality tradeoff, and usually needs separate evaluation.

No. ISIRO Runtime does not affect model accuracy. Weights are not quantized, approximated, retrained, or calibrated down.

No. ISIRO Runtime is an AI inference efficiency layer, not just model compression. A compact .tic representation reduces model footprint for associated reduction in cost and energy, and enterprises deploy the runtime layer: efficient execution on the inference path, lower memory movement without quantization, and TIC Shield for model protection at rest and in transit.

Bit-exact means weights decoded from a .tic file match the original model weights with no loss. Weights are compressed into .tic, then decoded on the chip during fused decode rather than loaded into or from off-chip memory. There is no quantization and no precision change. Decoded weights are verified against the original model with a signed hash manifest that can be regenerated from the original checkpoint.

ISIRO Runtime is different from those approaches. Quantization, pruning, and KV-cache optimization change what is stored or computed (lower precision, removed weights, or approximated cache values), often with a quality tradeoff. ISIRO Runtime reduces memory movement during execution while preserving the original model representation and model accuracy.

ISIRO Runtime sits between your models and your existing inference stack. Compile once into a compact, execution-native .tic representation, then deploy through ISIRO Runtime, which integrates with frameworks such as vLLM, TensorRT, and similar stacks.

AI inference is often limited by memory movement. ISIRO Runtime reduces how much model data moves through memory during execution, lowering energy use and infrastructure cost. On memory-bound workloads, teams can often serve the same model with fewer GPUs, smaller instances, or less memory per node.

ISIRO Runtime can be deployed in your existing cloud, on-prem, and edge environments.

Yes. ISIRO Runtime is available for evaluation in your environment without sharing your model. Request access to get started.