We’ve made the decision to invest in our own GPU rig for running inference models rather than relying on cloud services. This choice wasn’t made lightly; it’s based on a careful analysis of the costs, performance, and security implications. Here’s why we chose to go the on-prem route.
Cost Efficiency
Cloud services can be expensive, especially when running large-scale inference models. While they offer scalability and flexibility, the costs can quickly escalate, particularly if you’re running models that require high computational power. Our setup includes NVIDIA RTX 4090 and 5080 GPUs, which are powerful enough to handle most of our inference tasks without breaking the bank. By investing in these GPUs, we’ve achieved significant cost savings over time compared to renting cloud resources.
Performance and Latency
Latency is a critical factor in real-time applications. Cloud services introduce additional latency due to network delays, which can be problematic for applications requiring low-latency responses. Our on-prem GPU rig allows us to minimize this latency, ensuring that our inference models respond as quickly as possible. This is particularly important for clients who rely on real-time analytics and decision-making. The dual GPUs provide ample processing power to handle multiple requests simultaneously, further reducing wait times.
Data Control and Security
Data control and security are paramount, especially when dealing with sensitive client information. By running our inference models on-premises, we have complete control over our data. We can ensure that all client data remains within our secure environment, minimizing the risk of data breaches or unauthorized access. This level of control is often difficult to achieve with cloud services, where data may be stored across multiple servers and regions. We use vLLM to manage and optimize GPU resources efficiently, further enhancing our security posture.
Bottom line: Running our own GPU rig gives us better cost control, lower latency, and stronger data security than cloud inference services.