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Discovering and solving problems

Amazon just released their latest homegrown generative models, and these language models need to be on your radar if you're in the AI space. Although Anthropic's Claude still leads the pack in common performance benchmarks (see comments for benchmarks link), the Amazon models are coming in ~3x or more (!!) cheaper than leading models. This is huge for enterprises that need to scale the costs of deploying LLM-backed projects & services. Big release, still highlights the head start the hyperscalers have in this industry. #ai #llms #aws #anthropic #openai

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Stephen Gheysens

Discovering and solving problems

2w

Benchmarks on the new Nova models from AWS: https://aws.amazon.com/ai/generative-ai/nova/understanding/

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Godwin Josh

Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer

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Amazon's cost-effective approach likely leverages their vast infrastructure and optimized training pipelines, potentially employing techniques like model distillation and parameter pruning. The emphasis on cost reduction suggests a focus on efficiency metrics beyond benchmark scores, possibly prioritizing latency and inference throughput for real-world applications. Given the competitive landscape, how do you envision Amazon's models addressing the inherent bias and fairness challenges prevalent in large language models?

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