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Researchers from the University of Washington Introduce Fiddler: A Resource-Efficient Inference Engine for LLMs with CPU-GPU Orchestration

Marktechpost

Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. The post Researchers from the University of Washington Introduce Fiddler: A Resource-Efficient Inference Engine for LLMs with CPU-GPU Orchestration appeared first on MarkTechPost.

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SGLang: An Open-Source Inference Engine Transforming LLM Deployment through CPU Scheduling, Cache-Aware Load Balancing, and Rapid Structured Output Generation

Marktechpost

SGLang is an open-source inference engine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. Also,feel free to follow us on Twitter and dont forget to join our 75k+ ML SubReddit.

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Run AI Open Sources Run:ai Model Streamer: A Purpose-Built Solution to Make Large Models Loading Faster, and More Efficient

Marktechpost

Additionally, Run Model Streamer integrates natively with popular inference engines, eliminating the need for time-consuming model format conversions. Don’t Forget to join our 55k+ ML SubReddit. This versatility ensures that developers do not need to worry about compatibility issues, regardless of where their models are stored.

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Improved ML model deployment using Amazon SageMaker Inference Recommender

AWS Machine Learning Blog

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance.

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Allen Institute for AI Released olmOCR: A High-Performance Open Source Toolkit Designed to Convert PDFs and Document Images into Clean and Structured Plain Text

Marktechpost

Compatible with inference engines like vLLM and SGLang, allowing flexible deployment on various hardware setups. Also,feel free to follow us on Twitter and dont forget to join our 80k+ ML SubReddit. Improves language model training by increasing accuracy by 1.3 Check out the Training and toolkit code and Hugging Face collection.

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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning Blog

This enhancement allows customers running high-throughput production workloads to handle sudden traffic spikes more efficiently, providing more predictable scaling behavior and minimal impact on end-user latency across their ML infrastructure, regardless of the chosen inference framework.

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Host ML models on Amazon SageMaker using Triton: TensorRT models

AWS Machine Learning Blog

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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