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GoAutomate AI Institute
Canadian Sovereign AI, built for public benefit 🍁
Accessible · Responsible · Canadian‑governed
Advancing efficient, sovereign artificial intelligence for Canada — and sharing the methods with the world.
🏛️ Who we are
The GoAutomate AI Institute is a not‑for‑profit advancing Canadian sovereign artificial intelligence — AI models, tools, and research designed for Canadian organizations and aligned with Canadian values, ethics, and governance.
We don't believe Canada has to win only the race toward ever‑larger frontier models. Purpose‑built, efficient, Canadian‑governed models can deliver real outcomes in real environments — hospitals, health authorities, public institutions, businesses, researchers, and not‑for‑profits — while keeping people in control and accountable.
This organization is where we publish our open‑weight models, inference research, and technical reports.
At a glance
| Mission | Canadian sovereign AI, for public benefit |
| Model family | Terna — ternary‑weight models (Latin terni, "three each") |
| First release | Terna‑E2B — ternary distillation of Gemma‑4‑E2B (pre‑release) |
| Format | GGUF · runs on llama.cpp |
| Flagship report | TR‑2026‑001 · DOI 10.5281/zenodo.21110909 |
| Structure | Not‑for‑profit · open weights |
🔬 Our research bet: ternary
Modern AI isn't limited by intelligence — it's limited by the cost of storing and moving it. Most of the cost of running a language model is the memory bandwidth spent streaming billions of weights for every token, and the arithmetic of multiplying them.
Ternary representation constrains every weight to one of three values — { −1, 0, +1 } — and attacks both costs at once:
| ≈ 8–10× smaller | Multiply‑free | Capability preserved |
|---|---|---|
| Each weight carries ≈ 1.58 bits (log₂3) instead of 16 — roughly an order of magnitude below half precision, and about half the size of 4‑bit quantization. | A weight in { −1, 0, +1 } turns the expensive multiply into add, subtract, or skip. Roughly a third of the work disappears as structured sparsity. | Reached through distillation — a ternary student trained to reproduce a high‑precision teacher, so the constraint is learned, not imposed after the fact. Subtract cost, not intelligence. |
The payoff spans hardware generations: ternary's footprint lets capable models fit on memory‑constrained edge devices and revive legacy datacenter GPUs that modern models had outgrown — extending the useful life of existing silicon — while on modern accelerators the same savings mean far more concurrent users per device. It's an environmental story as much as a performance one: less energy per token, fewer accelerators per unit of served intelligence, and a slower path to e‑waste.
⭐ Featured release — Terna‑E2B
Terna‑E2B is our first release, and the first in the Terna family of ternary‑weight models (Terna, from Latin terni, "three each"). It is a ternary (≈1.6‑bit) distillation of Gemma‑4‑E2B, a capability‑dense model with a transparent, Western open‑weight lineage, distributed as GGUF for efficient local and datacenter serving.
⚠️ Pre‑release checkpoint. The published weights are an early checkpoint (
1B training tokens), shared to demonstrate the method and invite community evaluation. **Production weights (15B tokens) with full capability benchmarks will follow and replace them.** We defer quantitative capability claims to that release rather than over‑state a pre‑release checkpoint.
Browse the model repositories in this organization for files, quantizations, and usage.
🎯 Why Gemma‑4 as the base
- Exceptional intelligence‑per‑FLOP. An already capability‑dense family; ternary distillation compounds that efficiency.
- Elastic architecture. The E2B ("effective‑2B") design fits edge and multi‑tier deployment — and ternary lowers the floor of that range further.
- Provenance you can defend. For regulated and sovereignty‑sensitive sectors — healthcare, public sector, critical infrastructure — the provenance of a model's knowledge is a governance requirement, not a preference. A transparent, auditable, Western open‑weight base gives the resulting ternary model a clear, documentable lineage. Efficiency should not come at the cost of provenance.
⚙️ An engine built to spend ternary's footprint on throughput
Ternary's memory savings are only as valuable as a serving stack's ability to convert them into useful work. We maintain an inference engine (a fork of the open‑source llama.cpp lineage) tuned so footprint becomes concurrency and throughput:
- Footprint → concurrent contexts — small weights leave far more device memory for KV cache, so one accelerator serves many more simultaneous sessions.
- Hardware‑best matmul dispatch — the engine picks the path that fits the device by batch size, so capability‑dense models serve efficiently even on GPUs that lack dedicated matrix‑multiply units.
- Device‑side sampling and shared‑prefix / cross‑session cache reuse — agentic workloads pay for a large shared system prompt once, not once per user.
We validate this across deliberately different accelerator families — legacy datacenter GPUs and earlier‑generation tensor‑core GPUs — to prove the ternary‑plus‑engine approach spans hardware generations. Every serving optimization is held to a token‑identical correctness gate before it ships: throughput work never silently degrades output.
📄 Research
- TR‑2026‑001 — Ternary Foundations for Efficient, Sovereign AI — the mathematics of ternary, why Gemma‑4, the engine that turns footprint into throughput, and the implications for legacy and modern hardware. (Zenodo · CC‑BY‑4.0)
- TR‑2026‑002 — Quantization‑Aware Training and Ternary Weights: Method vs. Representation — clears the "QAT vs ternary" category error and places both on one efficiency curve.
Further reports — on serving engines across accelerator families, multi‑GPU parallelism for heterogeneous fleets, shared‑context concurrency, ternary at larger scales, and governable agentic orchestration — are on the Institute's roadmap.
🤝 What we stand for
| 🍁 Sovereignty | Reduce dependency on foreign‑controlled AI models and infrastructure; support Canadian laws, governance, and interests. |
| 🛡️ Responsibility | Steerable, explainable, accountable, secure systems that keep humans at the helm. |
| 🌐 Accessibility | Capable models, tools, and documentation available to organizations across Canada. |
| ⚡ Efficiency | Capability that scales down in cost as readily as it scales up in ability. |
📬 Get involved
The GoAutomate AI Institute welcomes collaboration with Canadian organizations, healthcare systems, public institutions, researchers, policymakers, and technology leaders who share our commitment to responsible and accessible AI.
