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Deploy decision models with llama.cpp on Amazon SageMaker AI

Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. This technical how-to serves OpenAI-compatible chat completions with the AWS Deep Learning Container for llama.cpp, then extends the runtime with SemIf to return probabilities over declared options on CPU or GPU.

We haven't written up this one. AWS Machine Learning has the full story — the link below goes straight to it.

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Andrej Karpathy says this AI gap is widening

Renowned AI researcher Andrej Karpathy says the gap is widening among the small subset of the population that uses AI.

  • 75% of world's population, 6 billion, minimal LLM contact
  • 1-2 billion casual/infrequent ChatGPT-like users
  • 5,000 people with inside access to frontier AI systems

Run multi-node inference workloads using Ray LLM Deep Learning Containers

Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one…

  • Run LLM inference across multiple nodes using Amazon EKS and Ray DLCs
  • Separate prefill and decode nodes to improve performance in complex workloads
  • Ray LLM DLC with vLLM, Ray Serve, NIXL, and EFA simplifies deployment

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