Why Kubernetes Struggles with Scaling Large Language Models
Kubernetes (k8s) is often the go-to for orchestrating applications in the cloud, but its traditional strengths don’t translate well to the unique world of large language models (LLMs). Let’s explore the reasons behind this incompatibility:
Kubernetes Relies on Outdated Scaling Metrics: Traditional scaling metrics, the bedrock of Kubernetes, fall short when dealing with the dynamic nature of LLMs. These metrics, focusing on resource utilization like GPU memory and compute time, don’t offer an accurate picture of actual LLM performance. For example, high GPU memory consumption might simply reflect large batching operations rather than genuine model processing, leading to misleading scaling decisions. Similarly, while GPU compute time seems intuitive, it fails to account for the variations in actual processing time caused by factors like batch size and cache utilization.
Kubernetes Neglects Concurrency’s Importance: Efficient LLM scaling hinges on carefully managing concurrency— the number of requests a model handles simultaneously. However, concurrency is not a primary consideration in Kubernetes’ design, leading to suboptimal resource allocation and potential performance bottlenecks. Simply adding more GPUs based on traditional metrics won’t necessarily enhance LLM performance if concurrency bottlenecks remain unaddressed.
Kubernetes Suffers from Sluggish Deployment Scaling: Scaling up LLM deployments with Kubernetes can be an exercise in patience. It can take an agonizingly long time, sometimes up to 20 minutes, for a server to be ready to handle requests after a scale-up decision. This delay stems from several time-consuming processes inherent in Kubernetes:
- Cloud Provisioning: Requesting and configuring new resources from the cloud provider takes time.
- Frontend Revisioning: Updating the frontend components to reflect the scaled-up backend can be slow.
- Model Loading: Downloading and loading large LLM models into memory on new instances is a significant bottleneck.
Concurrency-based scaling emerges as a more fitting approach for LLMs. This method prioritizes metrics like queue size, wait time, and batch size, dynamically adjusting resources to meet specific latency targets. However, realizing this advantage necessitates specialized infrastructure and a serving layer capable of interpreting and acting upon these metrics.