The Sovereign AI Launcher. Deploy, manage, and train state-of-the-art models on your own hardware. No DevOps required.
Running large models locally shouldn't require a PhD in CUDA drivers. Stackend audits your bare metal, matches it with compatible models, and handles the drivers automatically.
We analyze your CPU & GPU telemetry to recommend quantization levels that balance speed and accuracy automatically.
One-click setup for Training, RAG pipelines, Chat interfaces, or Coding assistants. All dependencies and drivers included.
Update, re-train (fine-tune), and wipe models from a single centralized dashboard. Avoid zombie processes.
Stackend automatically matches your hardware with the best open models.
* Performance metrics are estimated based on your hardware profile.
A purpose-built orchestration layer that bridges the gap between bare-metal hardware and modern AI inference.
This is the brain that audits your system resources (VRAM/RAM) in real-time and determines the optimal quantization for every model.
We leverage containers to isolate the AI environment. This ensures 100% reproducibility across servers without polluting the host OS with dependencies.
Powered by Optimized inference and GGUF technology. We optimize model weights for low-latency CPU/GPU execution, enabling near-native speeds on consumer hardware.
When you can't send data to the cloud, bring the cloud to your data. Stackend is designed for engineering teams in regulated industries.
Beta note: during onboarding, Stackend can optionally consult a hosted LLM to translate your
free-text intent into a model recommendation. This is opt-in via GROQ_API_KEY; without
it the recommender falls back to a fully local heuristic. Inference itself always runs on your
hardware.
Join the engineering teams deploying sovereign AI on their own terms.