Running AI and LLM Applications: Why Compute Matters

AI applications are not like web applications. A standard SaaS product has request-response patterns that are well-understood — a few hundred milliseconds, modest CPU, predictable memory. An LLM inference request can take seconds, saturate all available cores, and load gigabytes into memory per request.

Running AI workloads on general-purpose web hosting plans is a fast way to create a bad user experience. A shared CPU environment that throttles under sustained load will make your inference endpoint feel unreliable. Response times balloon. Requests time out. Users lose trust.

Dedicated vCPUs are the baseline requirement for AI workloads. When inference runs, it needs consistent access to compute. Shared CPU environments borrow cycles from other tenants, which introduces jitter into your response times. Dedicated compute means your model runs at full speed, every time.

Memory is equally important. Running a mid-size language model — anything in the 7B to 13B parameter range — requires substantial RAM for the model weights alone before you account for the KV cache, input tokenisation, and output generation. Undersized RAM causes the OS to swap, and swap is catastrophically slow for inference.

Data processing pipelines have different constraints but similar needs. Embedding generation, vector indexing, fine-tuning jobs, and batch inference all benefit from high CPU core counts and fast local storage. The Northstar VPS Compute plan provides 16 vCPUs, 64GB RAM, and 500GB of primary storage for exactly these workloads.

Latency to your data sources also matters. If your AI application queries a database or vector store on every inference request, the network round-trip adds directly to your response time. Hosting your application and its data on the same managed server eliminates this.

The economics of AI infrastructure are improving quickly, but compute is still the expensive part. Starting on the right-sized managed server — one that is pre-configured, monitored, and secured — lets you focus on the model and product rather than the machine it runs on.