Technical Overview

AI inference cost is exploding.

In the cloud the cost shows up in GPU bills, power, and cooling. On-prem it shows up in utilization, hardware planning, and the cost of scaling out. At the edge it shows up as battery drain, latency limits, and tighter device memory.

Modern accelerators are extremely fast at arithmetic. However, many workloads still struggle with cost, latency, and energy. The bottleneck is often not compute. It is moving data fast enough to keep those compute engines busy.

Every inference step pulls model data through memory, layer after layer, token after token. That traffic adds up as models grow.

Modern compute is already incredibly fast. The hard part is feeding it data fast enough.

This is the memory wall problem.

On typical accelerators, moving data from DRAM or HBM costs orders of magnitude more energy than movement in registers or on-chip SRAM. Depending on the memory hierarchy and hardware generation, an off-chip access can land in the hundreds to thousands of picojoules, while register operations are often fractions of a picojoule.

In memory-bound workloads, arithmetic units sit idle while waiting for those transfers.

Industry default: quantization

The dominant optimization path today is quantization: reduce precision, move fewer bytes, improve efficiency. It works and it has enabled real gains across inference systems.

However, quantization is lossy by design. It changes native precision and numerical behavior, so outputs are no longer bit-for-bit identical to the trained baseline causing approximation error which affects model accuracy.

A better direction: lossless compression

For many teams, approximation is not just a technical tradeoff. It is a risk. Finance, healthcare, defense, and regulated enterprise workloads often need efficiency without changing model behavior.

What if the model did not need to change? What if efficiency came from how the trained model is represented and executed, not from altering its weights or outputs?

Trained weights are not random. Model training and optimization leave structure in the coefficients. Lossless compression can exploit that structure, move fewer bytes, and expand back to the exact original checkpoint bit for bit. The hard part is doing it without materializing a full dense model in memory again, in a representation that decodes efficiently on the hardware for real-time inference.

ISIRO Runtime is built for that problem on the production inference path. It preserves bit-exact model output with no quantization, no approximation, and no model changes. The served model behaves the same as the original precision baseline.

Where ISIRO Runtime sits in your stack

At Isiro, we offer ISIRO Runtime™, an AI inference efficiency layer powered by our proprietary TIC™ (Tensor Inference Core) technology. Models are compiled once from formats such as .onnx, .safetensors, and .pt into a compact .tic execution-native representation. ISIRO Runtime serves from that compressed state during inference, reducing memory traffic and associated cost and energy while preserving bit-for-bit identical outputs.

TIC Shield™, an add-on, enables secure, controlled model execution by protecting model artifacts at rest, in transit, and in use.

ISIRO Runtime sits between models and existing inference stack as an efficiency layer. It serves the .tic artifact with the existing inference frameworks as targets.

During inference

During inference, weights stay compact in memory instead of being materialized as a full dense tensor. Packed bytes flow toward on-chip SRAM and registers. Fused decode runs right next to the matmul units that consume them.

ISIRO Runtime handles the on-device path for the .tic artifact such as load-time verification, compressed residency, fused decode, and kernel execution on the hot path. Adapters connect ISIRO Runtime to the inference frameworks you already use as orchestration targets, preserving the same request handling, batching, and scheduling.

Representative results

On-disk .tic footprint

Before is total bytes of Hugging Face .safetensors shards before compile. After is the compiled .tic artifact for the same instruct build. (GiB = 1024³ bytes.)

On evaluated BF16 LLM workloads, representative runs show about 30% lower memory traffic.

Kernel latency varies by layer shape and batch size on hardware such as the NVIDIA GeForce RTX 5090 (compute capability 12.0). At M = 1, representative large MLP projection layers show up to approximately 2× lower kernel latency than dense BF16 cuBLAS baselines in single-layer fused-linear microbenchmarks. That regime is often memory-bound, roughly the shape of many per-token decode steps. Other shapes and batch sizes land closer to parity in compute-bound regimes.

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