Pilardin now supports running language models on dedicated organizational GPUs. This means your teams can host models on hardware that belongs to your organization, keeping sensitive data inside your network while getting the performance benefits of dedicated compute. No more sharing external inference capacity or worrying about unpredictable latency during peak hours.
Setting this up is straightforward. In Pilardin, you can register GPU resources, assign them to specific language models, and set usage policies per team or department. The platform handles model loading, memory management, and request routing, so your engineers can focus on building applications rather than managing infrastructure. You can also monitor GPU utilization and model performance directly from the Pilardin dashboard.
This feature is particularly useful for organizations with strict data residency requirements or those running high-volume inference workloads. By using dedicated GPUs, you gain consistent throughput and the ability to fine-tune models on your own data without it ever leaving your environment. Pilardin continues to expand its on-premise and hybrid capabilities, and this GPU support is a key step in making advanced AI practical for everyday organizational use.