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LLMs
Trends
- 1Stanford paper claims method could reduce reliance on Nvidia memory chips●Is This the End of LLMs? This Stanford Paper Could Make Nvidia's Memory Chips Unnecessary
A Stanford research paper is drawing attention with the claim that a new technique could make Nvidia's high-bandwidth memory chips unnecessary for running large language models. The paper has sparked debate in the AI and semiconductor communities over whether such a shift is realistic, with some seeing it as potentially disruptive to Nvidia's business and others urging caution about unproven research claims.
Repos
- GoogleChrome/modern-web-guidance Keep your coding agent up to date with the latest web best practices
- Dodaios/ste80-humanizer STE80 Humanizer: agent skill that removes AI tells and rewrites text 80% of the way to ASD-STE100 (Simplified Technical
- ggml-org/llama.cpp LLM inference in C/C++
- hyperionaidev/smolmaxxing maxxing the ability to run different LLMs on the smallest devices possible
- Sandflow-ai/sand3d 3D modeling in Python, built for LLMs.
- RikiyaT/lm_understand-logs Complete logs of the experiments in the blog post 'LLMs can understand and write neural network weights'
- NeuraCryption/SPECTRA Compress any LLM in seconds, load it for inference and in one click you have an AI that could have taken hours of vLLM c
- AmSach/maple MAPLE: Manifold-Activated Pruning of Latent Experts for Sub-2-BPW MoE LLMs