Inference Scaling & Batching

Once a model is deployed, you need to handle variable demand efficiently. Some applications see steady, predictable traffic. Others experience dramatic spikes - a recommendation engine during a flash sale, or a chatbot when a product launches. Inference scaling means adjusting your serving capacity to match demand, ideally automatically. Horizontal scaling - adding more instances of your model - is the most common approach. Kubernetes-based orchestration with autoscaling policies can spin up additional serving pods within minutes. For GPU-based inference, this requires available GPU capacity, which can be a constraint during peak demand. Batching - grouping multiple inference requests together and processing them simultaneously - is one of the most effective ways to improve GPU utilisation and reduce per-request cost. A GPU that processes one request at a time wastes most of its capacity. Processing thirty requests in a batch uses the same hardware much more efficiently. Dynamic batching collects incoming requests over a short window and processes them together, balancing throughput against latency. For large language models specifically, techniques like continuous batching and speculative decoding have significantly improved serving efficiency. The practical challenge is balancing responsiveness with cost - you want enough capacity to handle peak load without paying for idle resources during quiet periods. Getting this right requires understanding your traffic patterns and investing in monitoring and autoscaling infrastructure.