Your data stays yours. Your model runs on hardware you control.
Sending queries to a cloud LLM means your data leaves your building. Every prompt, every document, every piece of commercially sensitive information processed by an external model is shared with infrastructure you don’t own, logged by providers you don’t control and billed per token whether it’s useful or not. For a growing number of organisations, that’s no longer an acceptable arrangement. G2 builds compact rackmount platforms that run AI inference on-premise so the model is yours, the data stays local and the per-token bill disappears.
The case for keeping inference in-house.
Cloud LLM costs scale with usage in ways that are easy to underestimate at the start. Token costs accumulate quickly across a team and the pricing is entirely outside your control. Cost is often the secondary concern. The primary one is that inference data the prompts, the context, the documents fed into a model travels to and is processed on shared global infrastructure. For legal, financial, healthcare and engineering organisations handling confidential information, that’s a risk.
Running inference on your own hardware eliminates both problems at once. The model runs locally, the data never leaves your network and the compute cost is fixed in the hardware you’ve already bought.
Rackmount platforms sized for the workload.
On-premise AI doesn’t require a data centre. Current GPU generations can run capable LLMs and vision models in 1U and 2U chassis that fit in an existing equipment rack or comms room. We specify the platform around the models you need to run, GPU VRAM, PCIe bandwidth, CPU memory channel configuration and storage throughput. The system is correctly sized from the start, not upgraded reactively when performance disappoints.
• Compact 1U and 2U inference platforms for standard rack environments
• Single and multi-GPU configurations matched to model size and throughput requirements
• Development workstations for teams building, fine-tuning, and validating models locally
• Computer vision platforms for industrial inspection, security, and process monitoring
Specified for continuous inference, not benchmarks.
An inference server running production workloads operates continuously at sustained GPU utilisation. Chassis selection, cooling capacity and power delivery need to be sized for that reality rather than for a short benchmark run. G2 systems are built with thermal headroom for the duty cycle, so performance is consistent in week twelve as it was on day one.
View our AI systems or Talk to an engineer about running AI on your own hardware→
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