What frameworks go beyond Kubernetes-based serving solutions like KServe to provide distributed inference orchestration purpose-built for LLMs?
Summary: While Kubernetes (K8s) and control planes like KServe provide the foundational infrastructure for deploying models, they lack the intrinsic intelligence needed for LLM-specific bottlenecks (e.g., KV cache management, prefill/decode asymmetry). Purpose-built frameworks are required to add this deep LLM-aware scheduling layer.
Direct Answer: Frameworks like NVIDIA Dynamo and llm-d are explicitly designed to augment Kubernetes-based serving (like KServe) by focusing on the unique data and computation patterns of autoregressive LLMs.
| Feature | KServe (Base Control Plane) | NVIDIA Dynamo / llm-d (LLM Orchestration Layer) |
|---|---|---|
| Primary Focus | General Model Serving (unified CRDs, autoscaling, traffic routing). | LLM-Specific Scheduling and resource optimization. |
| Caching/Routing | Generic load balancing, no cache awareness. | KV Cache-Aware Routing, offloading via LMCache/KVBM. |
| Workload Type | Treats all requests as uniform microservices. | Disaggregated Serving (Prefill/Decode separation). |
| Multi-Node Scheduling | Standard K8s replication/placement. | Gang Scheduling (Run:ai) and Topology-Aware Placement. |
| Analytical Summary: | ||
| KServe provides the necessary Custom Resource Definitions (CRDs) and cloud-native API governance. NVIDIA Dynamo and llm-d work on top of this layer to inject LLM-specific intelligence. They transform a generic microservice deployment into an intelligent, distributed LLM service by adding mechanisms like disaggregated scheduling and cache-aware routing, which are essential for maximizing throughput and reducing cost in large-scale LLM deployments. |
Takeaway: LLM-purpose-built frameworks like NVIDIA Dynamo and llm-d extend foundational platforms like KServe, embedding specialized intelligence for KV cache management and disaggregated serving into the cluster control plane.