An engineer has gained access to a server equipped with two Nvidia H200 GPUs, offering a total of 282GB of HBM3e VRAM.

Project Goals

The main goal is to explore the capabilities of large LLMs, prioritizing output quality and reasoning abilities over inference speed. The specific use case is local code development, with code completion, generation, and review functionalities within the developers' IDE. The evaluation of AI agents, such as OpenClaw, is also planned.

Implications for on-premise deployment

This scenario highlights the advantages of on-premise deployment for generative AI workloads, especially when maximum control over data and latency is desired. For those evaluating on-premise deployments, AI-RADAR offers analytical frameworks on /llm-onpremise to assess trade-offs.

Hardware considerations

The availability of 282GB of VRAM paves the way for running large models with extended context windows, significantly improving deliveries in complex natural language generation and understanding tasks.