Inference Frameworks
4 min read
The AI framework layer on top of Accelerated Computing Infrastructure that actually performs LLM serving, distributed inference, and fine-tuning. Covers single-node high-performance serving (vLLM), Kubernetes-native distributed inference (llm-d), MoE model processing, NVIDIA NeMo-based training, and operating criteria for reasoning and tool-call output boundaries of reasoning models.
vLLM Model ServingHigh-performance LLM inference with PagedAttention, Continuous Batching, Tensor/Pipeline Parallelism, Multi-LoRA hot-swapping.Reasoning & Tool-Call Output IntegrityClassifies why reasoning and tool calls leak into answers across the model, template, parser, API, and client layers, and prevents it with engine settings, validation, and a remediation layer.llm-d Distributed InferenceKubernetes-native distributed inference scheduler, KV Cache-aware routing, prefix cache optimization, Disaggregated Serving.HyperPod Inference OperatorSageMaker HyperPod managed KV cache, intelligent routing (prefix-aware/kv-aware), and DPD. How it differs from the Tiered Gateway and its role as the L2 routing layer.MoE Model ServingEfficient serving of Mixture of Experts models — Expert Parallelism, dynamic routing, memory optimization.NeMo FrameworkLarge-scale training and fine-tuning with NVIDIA NeMo, distributed training, EFA high-speed networking, checkpointing.
Reading Order
Reading in vLLM → Reasoning & Tool-Call Output Integrity → llm-d → HyperPod Inference Operator → MoE → NeMo order follows the progressive difficulty of "single-node optimization → output boundary operations → distributed inference → managed inference routing → large-scale MoE → training framework."