Fully Local AI Deployment
Cohere’s Command A+ and North Mini Code, alongside other current Canadian open-weight models, selected for each workload and running on computers you own. Deployment runs in a private cloud, on your own hardware, or fully air-gapped, supporting Canadian sovereign AI and reducing dependence on foreign AI clouds.
Using a closed cloud AI service sends prompts, code, and designs outside your environment. Retention and training terms vary by provider, plan, and contract. Local or air-gapped deployment keeps proprietary work from being transmitted to an external model provider. With Canadian models running on Canadian-controlled infrastructure, organizations can also keep more of their data, operations, and AI capability under Canadian control.
You Do Not Need a Data Center to Run Local AI
AI does not always require a new building, a new power connection, or a massive cooling system. For many businesses, local models can run on a workstation or compact server in the space they already have. The less a deployment relies on data center capacity, the smaller its environmental footprint. Keeping equipment in service longer reduces it further.
Less Land, Concrete, and Grid Pressure
A large AI data center brings a large construction footprint: land clearing, concrete and steel, cooling equipment, new transmission capacity, and continuous electricity demand. Those impacts arrive before the first prompt is processed. If your workload can run locally, you do not need to add that infrastructure.
Local AI Can Fit in the Room
A compact AI workstation such as NVIDIA DGX Spark can plug into an ordinary wall socket and run capable open models close to the people using them. It needs a desk, not a new building. That means less construction, no land cleared for your deployment, and a smaller physical footprint than a dedicated data-center build.
Work That Never Needs a Data Center
Every workload that runs on your own equipment is one that does not need capacity built for it somewhere else: no land, no cooling system, no new connection to the grid. It runs on the power and space you already have. Hardware kept in service longer, and recycled responsibly at the end, keeps the footprint smaller still.
Canadian Control, Leading Open Weights
We begin with Canadian open-weight models to keep more AI expertise, intellectual property, and strategic capability in Canada. When another model is the better fit, we can deploy leading current open weights on Canadian-controlled infrastructure while keeping your data and operating continuity under your control.
Own the Intelligence Layer
Sovereign AI means running, securing, and governing your AI on your own terms. Cohere calls it owning the intelligence layer instead of renting it. Open weights are what let you verify it: the architecture and the behavior are open to inspection. Deployment can sit in a private cloud you control, on hardware in your own building, or on a network with no route out at all. We pick the one the work needs. Sovereignty still depends on licensing, infrastructure, data handling, and governance.
Command A+ & North Mini Code
Cohere, founded in Toronto in 2019, builds the Command family of enterprise language models. Command A+, released in May 2026, its largest open model and open sourced under Apache 2.0 license: a 218B-parameter mixture of experts with 25B active per token, a 128K-token context window. North Mini Code, its agentic coding model, is far smaller at 30B parameters with 3B active, and fits on a single GPU. We choose between them by workload and by the hardware you have.
Qwen3.8
Alibaba’s Qwen team publishes open weights at sizes a business can actually afford to run. Qwen3.8-27B is Apache 2.0, reads text and images, and holds a 262,000-token context; quantized, it fits on a single 24 GB graphics card or a mid-range Mac. Qwen3.8-Flash-Next suits a shared server: only 6 billion of its parameters activate per token, so it answers quickly and serves several people at once, though the full weights need more memory and ship under Qwen’s own community license rather than Apache 2.0. We choose between them by workload, hardware, and licensing fit.
Run on Your Own Hardware
All-in-one AI workstations, inference servers we build to order in Canada, or hardware you already own, sized for your workload, number of users, and performance requirements.
Servers & Workstations
We can use hardware you already own, including compatible memory, reuse capable business machines, or specify a new workstation or server for your chosen models. Each vendor has its own acceleration path: MLX on Apple Silicon, CUDA on NVIDIA, ROCm on AMD, and oneAPI on Intel. Vulkan runs across all of them when a portable path is the better fit. Deployments range from a single-user workstation to server-class, multi-GPU systems for entire teams.
Apple Mac, AMD Ryzen AI Halo, NVIDIA DGX Spark
Complete workstations built to each vendor’s own first-party reference design: Apple Silicon Macs, AMD Ryzen AI Halo systems, and NVIDIA DGX Spark GB10 or GB300 workstations. The platform is matched to model size, user count, and the performance your workload needs.
Custom-Built Inference Servers
We build inference servers to order in Canada, choosing the accelerator to match the job: NVIDIA RTX PRO 6000, AMD Radeon AI PRO R9700, or Intel Arc Pro B70. Retired data-center inference cards are another option. They usually have years of useful work left, cost well below new, and put no fresh demand on a memory market where prices are already high.
Inference Runtimes & Model Management
Inference runtimes execute and serve the models. Model-management tools make them easier to download, configure, update, and expose through local APIs.
Linux, Windows & macOS
Production inference can run on Linux, Windows, or macOS, from an Apple Silicon workstation to bare-metal and virtualized servers. Fully offline, air-gapped configurations are available when data must remain inside your environment.
vLLM, SGLang, MLX, MLC LLM and llama.cpp
vLLM and SGLang serve high-throughput team workloads, while MLC LLM creates optimized runtimes across different devices. llama.cpp runs quantized models across common hardware, and MLX optimizes local inference on Apple Silicon. We select the runtime for the model, operating system, hardware, and number of users.
LM Studio, Ollama & Lemonade
These tools simplify model downloads, configuration, updates, and local serving. LM Studio provides a desktop GUI around runtimes such as llama.cpp, Ollama offers command-based model management and APIs, and Lemonade provides local setup and AMD acceleration.
Agentic Workflows
Turn local models into practical tools and controlled automation: private chat and knowledge, coding and business agents, and secured autonomous workflows.
Private Chat & Knowledge
Open WebUI gives your team a familiar multi-user chat with per-user access control. It can connect local models to approved documents, code repositories, vector databases, business tools, and private memory without sending that context to an external model provider.
Coding & Business Agents
OpenAI-compatible APIs let OpenClaw, Hermes agents, Claude Code, Codex, and internal business agents use models running on your hardware. Existing workflows can move from hosted AI to local models without rebuilding the whole toolchain.
Controlled Autonomous Work
We harden agent pipelines with prompt-injection defenses, least-privilege tool permissions, sandboxing and allowlists, audit logs, and human approval gates for consequential actions. Agents can automate useful work without receiving unrestricted access to everything around them.
Ready to get started?
Tell us about your environment, we’ll design a deployment that keeps your data yours.