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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset. FloTorch used these queries and their ground truth answers to create a subset benchmark dataset.

Benchmark 105
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Pixtral-12B-2409 is now available on Amazon Bedrock Marketplace

AWS Machine Learning

Overview of Pixtral 12B Pixtral 12B, Mistrals inaugural VLM, delivers robust performance across a range of benchmarks, surpassing other open models and rivaling larger counterparts, according to Mistrals evaluation. Performance metrics and benchmarks Pixtral 12B is trained to understand both natural images and documents, achieving 52.5%

Benchmark 100
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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3

APIs 119
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Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart 

AWS Machine Learning

This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. Additionally, models are fully sharded on the supported instance.

Benchmark 129
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Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

AWS Machine Learning

Consider benchmarking your user experience to find the best latency for your use case, considering that most humans cant read faster than 225 words per minute and therefore extremely fast response can hinder user experience. In such scenarios, you want to optimize for TTFT. Users prefer accurate responses over quick but less reliable ones.

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

Amazon Bedrock , a fully managed service offering high-performing foundation models from leading AI companies through a single API, has recently introduced two significant evaluation capabilities: LLM-as-a-judge under Amazon Bedrock Model Evaluation and RAG evaluation for Amazon Bedrock Knowledge Bases. 0]}-{evaluator_model.split('.')[0]}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"

Metrics 104
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Mistral-Small-24B-Instruct-2501 is now available on SageMaker Jumpstart and Amazon Bedrock Marketplace

AWS Machine Learning

Performance metrics and benchmarks According to Mistral, the instruction-tuned version of the model achieves over 81% accuracy on Massive Multitask Language Understanding (MMLU) with 150 tokens per second latency, making it currently the most efficient model in its category. It doesnt support Converse APIs or other Amazon Bedrock tooling.

APIs 94