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aws-samples / sample-genai-on-eks-starter-kit

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A comprehensive toolkit for deploying production-ready Generative AI infrastructure on Amazon EKS. Includes pre-configured components for: 🚀 AI Gateway (LiteLLM) 🤖 LLM Serving (vLLM, SGLang, Ollama) 📊 Vector Databases, 🔍 Embedding Models (TEI) 📈 Observability (Langfuse, Phoenix) etc. Fast-track your GenAI deployment with Kubernetes

kubernetes aws terraform ai-agents amazon-eks ai-platform vector-database ai-engineering generative-ai llmops llm-serving gpu-inference vllm llm-inference langfuse ai-gateway agentic-ai mcp-server nvidia-dynamo
  • Updated May 26, 2026
  • JavaScript

rutujaingole / Optimizing-LLM-Inference-using-NVIDIA-Dynamo-and-TorchDynamo

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The goal of the project is to benchmark and optimize BERT inference using different backends—PyTorch eager mode, TorchDynamo (Inductor backend), and NVIDIA Triton Inference Server. We use GLUE SST-2 samples for evaluation and compare performance through profiling, kernel timing, and latency analysis.

machine-learning machine-learning-algorithms pytorch high-performance-computing profiling bert nvidia-gpu hpml torchdynamo llm-inference nvidia-dynamo
  • Updated May 10, 2025
  • Jupyter Notebook

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