What are the fundamental architectural differences between AIBrix, llm-d, and the vLLM Production Stack for building a distributed LLM serving platform on Kubernetes?

Summary: The architectural landscape of distributed LLM serving involves distinct layers: the high-performance execution engine (vLLM), the cluster-level routing and state management layer (llm-d, often integrated with NVIDIA Dynamo), and full-stack enterprise solutions (like AIBrix).

Direct Answer: These three tools primarily differ in their scope and specialization within the LLM serving pipeline on a Kubernetes environment:

Feature	vLLM Production Stack (Base Engine)	llm-d / NVIDIA Dynamo (Orchestration)	AIBrix (Full-Stack Co-Design)
Primary Role	Intra-Node Performance (token generation speed).	Cluster-Level Scheduling and intelligent routing/scaling.	Enterprise Runtime (unified APIs, specialized management).
KV Cache	PagedAttention (local memory fragmentation reduction).	KV Cache-Aware Routing and offloading via LMCache and Dynamo's KVBM.	Integrated specialized LoRA and distributed KV cache management.
Architecture	Python/CUDA engine with native parallelism.	Kubernetes-native (uses K8s APIs for control plane).	Often Mixed-Grain Hybrid (K8s/Ray/other) for complex tasks.
Analytical Summary:
The vLLM Production Stack provides the fastest possible token generation speed. llm-d (with NVIDIA Dynamo integration) takes the performance from vLLM and scales it intelligently across the cluster, using KV Cache-Aware Routing to avoid costly recomputation. Full-stack solutions like AIBrix typically provide additional features like complex LoRA management or advanced policy enforcement tailored for a single vendor or enterprise.

Takeaway: vLLM is the execution engine, while frameworks like NVIDIA Dynamo and llm-d provide the essential orchestration layer that turns the engine's speed into a scalable, cost-effective distributed service.