Tag: Inference
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Hacker News: Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inference
Source URL: https://cerebras.ai/blog/llama-405b-inference/ Source: Hacker News Title: Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inference Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses breakthrough advancements in AI inference speed, specifically highlighting Cerebras’s Llama 3.1 405B model, which showcases significantly superior performance metrics compared to traditional GPU solutions. This…
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AWS News Blog: AWS Lambda SnapStart for Python and .NET functions is now generally available
Source URL: https://aws.amazon.com/blogs/aws/aws-lambda-snapstart-for-python-and-net-functions-is-now-generally-available/ Source: AWS News Blog Title: AWS Lambda SnapStart for Python and .NET functions is now generally available Feedly Summary: AWS Lambda SnapStart boosts Python and .NET functions’ startup times to sub-second levels, often with minimal code changes, enabling highly responsive and scalable serverless apps. AI Summary and Description: Yes Summary: The announcement…
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The Register: Nvidia continues its quest to shoehorn AI into everything, including HPC
Source URL: https://www.theregister.com/2024/11/18/nvidia_ai_hpc/ Source: The Register Title: Nvidia continues its quest to shoehorn AI into everything, including HPC Feedly Summary: GPU giant contends that a little fuzzy math can speed up fluid dynamics, drug discovery SC24 Nvidia on Monday unveiled several new tools and frameworks for augmenting real-time fluid dynamics simulations, computational chemistry, weather forecasting,…
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Hacker News: Qwen2.5 Turbo extends context length to 1M tokens
Source URL: http://qwenlm.github.io/blog/qwen2.5-turbo/ Source: Hacker News Title: Qwen2.5 Turbo extends context length to 1M tokens Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the introduction of Qwen2.5-Turbo, a large language model (LLM) that significantly enhances processing capabilities, particularly with longer contexts, which are critical for many applications involving AI-driven natural language…
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Hacker News: Don’t Look Twice: Faster Video Transformers with Run-Length Tokenization
Source URL: https://rccchoudhury.github.io/rlt/ Source: Hacker News Title: Don’t Look Twice: Faster Video Transformers with Run-Length Tokenization Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a novel approach called Run-Length Tokenization (RLT) aimed at optimizing video transformers by eliminating redundant tokens. This content-aware method results in substantial speed improvements for training and…
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Hacker News: BERTs Are Generative In-Context Learners
Source URL: https://arxiv.org/abs/2406.04823 Source: Hacker News Title: BERTs Are Generative In-Context Learners Feedly Summary: Comments AI Summary and Description: Yes Summary: The paper titled “BERTs are Generative In-Context Learners” explores the capabilities of masked language models, specifically DeBERTa, in performing generative tasks akin to those of causal language models like GPT. This demonstrates a significant…
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Cloud Blog: Data loading best practices for AI/ML inference on GKE
Source URL: https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke/ Source: Cloud Blog Title: Data loading best practices for AI/ML inference on GKE Feedly Summary: As AI models increase in sophistication, there’s increasingly large model data needed to serve them. Loading the models and weights along with necessary frameworks to serve them for inference can add seconds or even minutes of scaling…
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Cloud Blog: 65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models
Source URL: https://cloud.google.com/blog/products/containers-kubernetes/gke-65k-nodes-and-counting/ Source: Cloud Blog Title: 65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models Feedly Summary: As generative AI evolves, we’re beginning to see the transformative potential it is having across industries and our lives. And as large language models (LLMs) increase in size — current models are reaching…