tag: Llm-Inference · 4 items
- Platform/SRE — Learn: LLM-D is an emerging Kubernetes-native distributed inference project worth tracking as AI workloads increasingly land on shared clusters; no GA release or concrete migration action yet.
- CI/CD — Skip
- Leader — Learn: Signals a maturing pattern for running LLM inference on Kubernetes infrastructure; worth monitoring as a potential build-vs-buy input for AI serving strategy, but no decision point yet.
- Platform/SRE — Plan: A new GA EKS managed add-on that replaces round-robin LB with real-time GPU-signal-aware routing (KV cache, queue depth, LoRA residency) for HyperPod clusters — evaluate adopting this quarter if your team runs SageMaker HyperPod inference workloads on EKS.
- CI/CD — Skip
- Leader — Learn: AWS’s inference gateway pattern (single endpoint, multi-model routing, hardware-aware scheduling) signals a maturing managed alternative to self-built vLLM fleet management — worth tracking as it informs the build-vs-buy framing for LLM serving infrastructure.
- Platform/SRE — Plan: If you run LLM inference workloads on SageMaker HyperPod, this GA feature cuts scale-out time ~60% by pre-loading model weights to NVMe and pre-pulling container images; evaluate adding a modelCacheConfig block to InferenceEndpointConfig resources this quarter.
- CI/CD — Skip
- Leader — Learn: HyperPod’s new model caching substantially narrows the cold-start penalty for large-model inference, which is relevant context if evaluating HyperPod as the org’s managed inference platform versus self-managed alternatives.
- Platform/SRE — Learn: A new project enabling distributed LLM inference natively on Kubernetes—worth evaluating for teams planning AI/ML serving infrastructure, but no GA status confirmed and no enrichment signals to anchor a higher verdict.
- CI/CD — Skip
- Leader — Learn: Signals a maturing ecosystem for running LLM inference on existing Kubernetes infrastructure, relevant to strategy around AI workload hosting; no near-term decision required.