News from the AI & ML world
DeeperML
Michael Nuñez@AI News | VentureBeat
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Amazon Web Services (AWS) has announced significant advancements in its AI coding and Large Language Model (LLM) infrastructure. A key highlight is the introduction of SWE-PolyBench, a comprehensive multi-language benchmark designed to evaluate the performance of AI coding assistants. This benchmark addresses the limitations of existing evaluation frameworks by assessing AI agents across a diverse range of programming languages like Python, JavaScript, TypeScript, and Java, using real-world scenarios derived from over 2,000 curated coding challenges from GitHub issues. The aim is to provide researchers and developers with a more accurate understanding of how well these tools can navigate complex codebases and solve intricate programming tasks involving multiple files.
The latest Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4, further enhances LLM capabilities. This version supports a wider array of open-source models, including Meta’s Llama 4 models and Google’s Gemma 3, providing users with more flexibility in model selection. LMI v15 delivers significant performance improvements through an async mode and support for the vLLM V1 engine, resulting in higher throughput and reduced CPU overhead. This enables seamless deployment and serving of large language models at scale, with expanded API schema support and multimodal capabilities for vision-language models.
AWS is also launching new Amazon EC2 Graviton4-based instances with NVMe SSD storage. These compute optimized (C8gd), general purpose (M8gd), and memory optimized (R8gd) instances offer up to 30% better compute performance and 40% higher performance for I/O intensive database workloads compared to Graviton3-based instances. They also include larger instance sizes with up to 3x more vCPUs, memory, and local storage. These instances are ideal for storage intensive Linux-based workloads including containerized and micro-services-based applications built using Amazon Elastic Kubernetes Service(Amazon EKS),Amazon Elastic Container Service(Amazon ECS),Amazon Elastic Container Registry(Amazon ECR), Kubernetes, and Docker, as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.
ImgSrc: venturebeat.com
References :
The latest Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4, further enhances LLM capabilities. This version supports a wider array of open-source models, including Meta’s Llama 4 models and Google’s Gemma 3, providing users with more flexibility in model selection. LMI v15 delivers significant performance improvements through an async mode and support for the vLLM V1 engine, resulting in higher throughput and reduced CPU overhead. This enables seamless deployment and serving of large language models at scale, with expanded API schema support and multimodal capabilities for vision-language models.
AWS is also launching new Amazon EC2 Graviton4-based instances with NVMe SSD storage. These compute optimized (C8gd), general purpose (M8gd), and memory optimized (R8gd) instances offer up to 30% better compute performance and 40% higher performance for I/O intensive database workloads compared to Graviton3-based instances. They also include larger instance sizes with up to 3x more vCPUs, memory, and local storage. These instances are ideal for storage intensive Linux-based workloads including containerized and micro-services-based applications built using Amazon Elastic Kubernetes Service(Amazon EKS),Amazon Elastic Container Service(Amazon ECS),Amazon Elastic Container Registry(Amazon ECR), Kubernetes, and Docker, as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.
ImgSrc: venturebeat.com
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References :
- venturebeat.com: Amazon’s SWE-PolyBench just exposed the dirty secret about your AI coding assistant
- www.marktechpost.com: AWS Introduces SWE-PolyBench: A New Open-Source Multilingual Benchmark for Evaluating AI Coding Agents