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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 109
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Benchmarking customized models on Amazon Bedrock using LLMPerf and LiteLLM

AWS Machine Learning

Amazon Bedrock Custom Model Import simplifies deployments of custom models by offering a straightforward API for model deployment and invocation. Before deploying these models in production, its crucial to evaluate their performance using benchmarking tools. A key advantage of LLMPerf is wide support of foundation model APIs.

Benchmark 113
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Accelerated PyTorch inference with torch.compile on AWS Graviton processors

AWS Machine Learning

You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.

Benchmark 122
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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

AWS Machine Learning

Business use case After its public release, DeepSeek-R1 model, developed by DeepSeek AI , showed impressive results across multiple evaluation benchmarks. These recipes have undergone extensive benchmarking, testing, and validation to provide seamless integration with the SageMaker training and fine-tuning processes.

Scripts 72
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Customize Amazon Nova models to improve tool usage

AWS Machine Learning

However, as industries require more adaptive, decision-making AI, integrating tools and external APIs has become essential. Expanding LLM capabilities with tool use LLMs excel at natural language tasks but become significantly more powerful with tool integration, such as APIs and computational frameworks.

APIs 103
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Transition your Amazon Forecast usage to Amazon SageMaker Canvas

AWS Machine Learning

You can also either use the SageMaker Canvas UI, which provides a visual interface for building and deploying models without needing to write any code or have any ML expertise, or use its automated machine learning (AutoML) APIs for programmatic interactions.

APIs 116
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Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. Solution overview The solution comprises two main steps: Generate synthetic data using the Amazon Bedrock InvokeModel API.

APIs 77