Sovereign LLMs built for your hardware, not theirs.
We build sovereign AI LLMs — models you download, run, and control on your own infrastructure. The Atlas-LLM family uses Mixture of Experts architecture optimized for local deployment, so your prompts never leave your machine. True AI sovereignty means you own the weights, you own the inference, you own your intelligence.
Why Sovereign AI?
Because intelligence shouldn't be a subscription. True AI sovereignty means decoupling your organization's capability from third-party cloud providers, opaque model updates, and data harvesting terms of service.
Why build our own models?
Off-the-shelf models were not designed for local-first deployment. We have architected our MoE models from the ground up for privacy, efficiency, and real-world performance.
Privacy by Design
Every model is optimized to run entirely on your hardware. Your prompts never leave your machine, your data stays yours.
MoE Efficiency
Mixture of Experts architecture activates only the relevant experts per token. Massive inference savings with full quality.
Hardware Optimized
Quantization-first design. Run 7B models on laptops, 70B models on desktops. Designed for the hardware you already own.
MoE Architecture
Mixture of Experts divides the network into specialized modules. Only 2-3 experts activate per token, enabling massive parameter counts with affordable compute.
Three sizes. One architecture.
Each model uses the same MoE foundation, scaled for different hardware tiers. Choose based on your setup - they share the same training and optimization approach.
Atlas-LLM Compact
SmallLightweight MoE for resource-constrained environments. Runs on laptops, older hardware, edge devices.
Our compact model is designed for instant responsiveness. With aggressive quantization and efficient routing, it delivers fast inference while maintaining coherent outputs. Perfect for developers who need AI assistance on the go without carrying a beefy workstation.
Hardware Requirements
Architecture
Performance
Atlas-LLM Standard
MediumBalanced MoE for desktop and workstation deployment. Best quality-to-speed ratio for daily use.
The standard model is our flagship for most users. It strikes the ideal balance between output quality and inference cost. Designed for developers who want competent assistance without GPU envy. Handles complex tasks, multi-file contexts, and extended conversations with ease.
Hardware Requirements
Architecture
Performance
Atlas-LLM Pro
LargeMaximum capability MoE for workstations and small servers. State-of-the-art local inference.
The Pro model delivers capabilities competitive with frontier models while staying fully local. Designed for power users, small teams, and organizations that need serious AI capability without cloud dependencies. Handles complex reasoning, long documents, and multi-step workflows.
Hardware Requirements
Architecture
Performance
Model comparison
| Model | Params | Context | GPU VRAM | Speed | Best For |
|---|---|---|---|---|---|
| Atlas-LLM Compact | ~1B | 8K tokens | 2GB | 50+ tok/s | Mobile, Edge, Quick tasks |
| Atlas-LLM Standard | ~7B | 32K tokens | 6GB | 30+ tok/s | Daily coding, General tasks |
| Atlas-LLM Pro | ~70B | 128K tokens | 24GB | 15+ tok/s | Complex reasoning, Full codebase |
How they are trained
All three models share the same training pipeline - quality over quantity at every stage.
Curated Datasets
Trained on hand-curated data with emphasis on technical accuracy. No scraped internet dumps, no synthetic shortcuts. Quality annotations and human preference data.
Domain Adaptation
Further fine-tuned on developer workflows, documentation, and codebases. Models understand your domain - not just language.
Join the Research Program
Get early access to model weights, training updates, and direct influence on development priorities.