Cloud, on-prem, or at the edge.
Same model, same governance, same control plane — sized and operated for the environment that fits your security, latency, and cost profile.
- On-prem for full data sovereignty
- Private cloud (AWS · Azure · GCP) for elastic scale
- Edge for offline + low-latency environments
At LLM.co, we offer LLM-in-a-Box: pre-configured, pre-trained hardware appliances that allow you to run private large language models locally—on-premise, offline, and behind your firewall. Whether for regulated industries, sensitive data, or air-gapped environments, these boxes bring intelligence directly to your environment with zero API dependencies and full data ownership.
What's Inside The Box?
Our portable LLM appliance comes preloaded with: A secure containerized LLM runtime (Docker/Kubernetes), Fine-tuned open-source models (e.g., LLaMA, Mistral, Phi, Mixtral, or others), Vector database + semantic search engine, Embedded RAG pipeline (Retrieval-Augmented Generation), Optional low-latency web UI or chat interface, Encryption, access control, and audit logging.
Specs vary by configuration, but typical units include: High-core CPU or dedicated GPU (NVIDIA A100/H100 or RTX-class), 32GB–128GB RAM, 1–8TB NVMe SSD, Optimized for low-latency inference of 7B–70B parameter models.
Air-Gapped by Design
Deploy in completely offline environments with zero external dependencies.
Fast Deployment
Ready-to-use appliances can be delivered, configured, and running in hours.
Own Your Stack
Run your own model. No OpenAI, no cloud APIs, no 3rd-party logging.
When it Comes to LLMs Hardware Isn't Everything
While local LLM hardware unlocks unprecedented privacy and control, it's not a silver bullet. Some important limitations include:
Hardware Constraints = Model Size Limits: Running a 7B–13B model is feasible on a single device. Running GPT-4-scale models locally? Not so much—unless you're investing in datacenter-grade clusters.
Inference Speed vs. Quality Tradeoff: Larger models tend to be slower or outright unusable on edge hardware, especially with large context windows or long documents.
Updating & Fine-Tuning Is Not Plug-and-Play: Fine-tuning or adding new capabilities to on-device models often requires retraining or careful prompt engineering—tasks not easily handled without technical expertise.
Edge Alone May Not Be Enough: For best results, many organizations pair on-prem edge LLMs with secure cloud models—a hybrid AI architecture that balances performance, cost, and compliance.
Go Hybrid When it Matters
The future of enterprise AI is hybrid—private models where you need them, public power where you trust it.
Use your LLM-in-a-Box for: On-site document analysis, Internal Q&A with no data egress, Offline summarization or compliance workflows
Pair with secure cloud or VPC models for: High-volume or large-context inference, Advanced reasoning or multi-agent orchestration, Centralized knowledge base access with distributed AI endpoints
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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