What if your AI agents ran fully local with zero cloud dependency?
With Model HQ, build no code agents that analyze contracts, extract key terms with RAG, and run batch workflows directly on device.
Private. Practical. Enterprise ready.
Download Model HQ and start building today
Model HQ by LLMWare lets you create private, on-device bots with your own prompts, documents, and UI.
Works offline.
Easy to share.
Built for real teams.
What if sentiment and emotion AI ran entirely on your device?
Model HQ by LLMWare lets you build agentic workflows for image and text classification with no code, no Wi Fi, and full privacy. Watch the demo and explore on device AI.
Link in the comments!
On-device AI should be easy to use.
In this walkthrough, LLMWare shows how Model HQ lets teams run GPU and NPU models locally, query documents with citations, customize prompts, and work without Wi-Fi.
Watch the demo and explore private, on-device AI with Model HQ.
How do you choose the right LLM before deployment?
Model HQ by LLMWare lets teams test and compare models locally with no code and no Wi Fi. Generate custom test sets, measure quality and latency, and validate models for real workflows.
Watch the demo and try Model HQ.
Everyone wants AI on their internal docs.
Almost no one wants to ship that data to external APIs.
Thatโs the blocker.
Local AI changes the game.
No uploads. No new exposure paths.
For many teams, this is when AI finally becomes usable.
Stop guessing. Test AI models locally and deploy with certainty.
In this walkthrough, we will show that Model HQ by LLMWare lets you download and test 150+ AI models on a no-code, fully local platform. Compare accuracy, speed, and hallucination behavior across NPU and GPU.
Grateful to Felippe Motta (Sr. Director, Product & Marketing @CORSAIR ) for the support and encouragement.
Always motivating to hear from leaders building at scale.
Just built a contract analysis AI agent with Model HQ - no code needed!
Started with a basic template, customized it for music licensing agreements, and deployed it across multiple docs in minutes.
All running locally for max security.
https://t.co/0AkqigfNmp
Model HQ just dropped a game-changing hybrid inferencing demo!
Run LLMs seamlessly between AI PCs and private servers with zero cloud dependency.
Key patterns we tested:
โข Local AI PC inference
โข Server-side API calls
โข Hybrid agent deployment
Best part? No per-token fees.
Just tested Model HQ's new hybrid inferencing - game-changing flexibility for enterprise AI.
Run workloads on local AI PCs or private servers, zero cloud dependency.
See how we built a secure RAG system with on-device inference + server vector store.
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Just ran Phi-4 14B on three different setups using Model HQ by @llmware : Xeon CPU server, Lunar Lake NPU, and GPU.
Zero cloud costs, fully local inference across all modes.
Perfect for enterprise deployment flexibility.
{ FULL ARTICLE IN THE COMMENTS }
Just ran Phi-4 (14B) across server, NPU & GPU using Model HQ.
Zero cloud costs, full local inference.
Server: Xeon
CPU-only NPU: Intel Core Ultra 9
GPU: Same machine, different mode
Results? Smooth inference everywhere.
{ LINK IN THE COMMENTS }