Efficient, quality-preserving AI inference – on the hardware you already own

3SWAI develops software that makes AI inference cheaper, more energy-efficient, and deployable on-premises and at the edge – across GPUs and CPUs, with no cloud dependency. Our quantization preserves model quality at low bit-widths, so organizations can run capable models on hardware they already have.

What we do
– Lower inference cost and energy: on current internal projections, roughly 2× lower total cost and energy per token versus a comparable high-end GPU, and about 2× faster than standard CPU inference.
– Preserve model quality: quantization that holds accuracy at low bit-widths, benchmarked against a strong GGUF Q4_K_M (imatrix) baseline.
– Deploy anywhere: model-agnostic, framework-compatible (TensorFlow, PyTorch), on-prem, edge, or existing servers – ideal for data-sovereign and regulated environments.

Where it helps
On-prem and edge deployments, high-volume latency-tolerant workloads, and any team wanting to cut inference cost or keep data in-house – healthcare, finance, public sector.

A note on our figures
All performance numbers are current internal projections, pending independent validation (expected autumn 2026).

Looking ahead
We're building the optimization layer for efficient, sustainable AI inference, and we're looking for pilot partners, integration partners, and investors. If that's you, get in touch.

3SWAI

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3SWAI

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