Experimental News Clipping Site

Tag: quantization

  • Hacker News: SVDQuant+NVFP4: 4× Smaller, 3× Faster FLUX with 16-bit Quality on Blackwell GPUs

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    Kurt Seifried
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    Source URL: https://hanlab.mit.edu/blog/svdquant-nvfp4 Source: Hacker News Title: SVDQuant+NVFP4: 4× Smaller, 3× Faster FLUX with 16-bit Quality on Blackwell GPUs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the release of SVDQuant, a new low-precision quantization paradigm that supports NVIDIA’s NVFP4 architecture on Blackwell GPUs. It highlights significant improvements in model accuracy,…

  • The Register: DeepMind working on distributed training of large AI models

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/02/11/deepmind_distributed_model_training_research/ Source: The Register Title: DeepMind working on distributed training of large AI models Feedly Summary: Alternate process could be a game changer if they can make it practicable Is distributed training the future of AI? As the shock of the DeepSeek release fades, its legacy may be an awareness that alternative approaches…

  • Simon Willison’s Weblog: Using pip to install a Large Language Model that’s under 100MB

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Feb/7/pip-install-llm-smollm2/ Source: Simon Willison’s Weblog Title: Using pip to install a Large Language Model that’s under 100MB Feedly Summary: I just released llm-smollm2, a new plugin for LLM that bundles a quantized copy of the SmolLM2-135M-Instruct LLM inside of the Python package. This means you can now pip install a full LLM! If…

  • Hacker News: How to Run DeepSeek R1 Distilled Reasoning Models on RyzenAI and Radeon GPUs

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    Kurt Seifried
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    Source URL: https://www.guru3d.com/story/amd-explains-how-to-run-deepseek-r1-distilled-reasoning-models-on-amd-ryzen-ai-and-radeon/ Source: Hacker News Title: How to Run DeepSeek R1 Distilled Reasoning Models on RyzenAI and Radeon GPUs Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the capabilities and deployment of DeepSeek R1 Distilled Reasoning models, highlighting their use of chain-of-thought reasoning for complex prompt analysis. It details how…

  • Hacker News: Running DeepSeek R1 Models Locally on NPU

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    Kurt Seifried
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    Source URL: https://blogs.windows.com/windowsdeveloper/2025/01/29/running-distilled-deepseek-r1-models-locally-on-copilot-pcs-powered-by-windows-copilot-runtime/ Source: Hacker News Title: Running DeepSeek R1 Models Locally on NPU Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses advancements in AI deployment on Copilot+ PCs, focusing on the release of NPU-optimized DeepSeek models for local AI application development. It highlights how these innovations, particularly through the use…

  • Hacker News: voyage-code-3

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    Kurt Seifried
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    Source URL: https://blog.voyageai.com/2024/12/04/voyage-code-3/ Source: Hacker News Title: voyage-code-3 Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents voyage-code-3, a new embedding model optimized for code retrieval that significantly outperforms existing models in both performance and cost-efficiency. The introduction of Matryoshka learning and advanced quantization techniques allows for reduced storage requirements without compromising…

  • Simon Willison’s Weblog: microsoft/phi-4

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    Kurt Seifried
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    Source URL: https://simonwillison.net/2025/Jan/8/phi-4/ Source: Simon Willison’s Weblog Title: microsoft/phi-4 Feedly Summary: microsoft/phi-4 Here’s the official release of Microsoft’s Phi-4 LLM, now officially under an MIT license. A few weeks ago I covered the earlier unofficial versions, where I talked about how the model used synthetic training data in some really interesting ways. It benchmarks favorably…

  • Hacker News: I Run LLMs Locally

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    Source URL: https://abishekmuthian.com/how-i-run-llms-locally/ Source: Hacker News Title: I Run LLMs Locally Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses how to set up and run Large Language Models (LLMs) locally, highlighting hardware requirements, tools, model choices, and practical insights on achieving better performance. This is particularly relevant for professionals focused on…

  • Simon Willison’s Weblog: Trying out QvQ – Qwen’s new visual reasoning model

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    Source URL: https://simonwillison.net/2024/Dec/24/qvq/#atom-everything Source: Simon Willison’s Weblog Title: Trying out QvQ – Qwen’s new visual reasoning model Feedly Summary: I thought we were done for major model releases in 2024, but apparently not: Alibaba’s Qwen team just dropped the Apache2 2 licensed QvQ-72B-Preview, “an experimental research model focusing on enhancing visual reasoning capabilities". Their blog…

  • Hacker News: Fast LLM Inference From Scratch (using CUDA)

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    Source URL: https://andrewkchan.dev/posts/yalm.html Source: Hacker News Title: Fast LLM Inference From Scratch (using CUDA) Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text provides a comprehensive overview of implementing a low-level LLM (Large Language Model) inference engine using C++ and CUDA. It details various optimization techniques to enhance inference performance on both CPU…