While SOTA LLMs are too large to run on laptops, quantization is a technique that reduces LLMs’ computational and memory requirements. Quantization reduces a model’s size and speeds up processing by converting its parameters from 32-bit to lower-precision formats like 16-bit or
COMPUTING
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New Hub feature displays carbon emissions from model training
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New feature on the Hub! Carbon emissions emitted during training now show up on the model card!
(requires model authors to fill that info first) Hopes it will prompt more people to show the carbon emissions of their model training! Thanks a lot to the team who pushed -
Axelera AI CEO Discusses M.2 Accelerator and Edge Computing
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Our CEO, @FabAroundEU
, discusses Axelera AI's journey, our M.2 accelerator, and the latest in AI acceleration technology in an interview with @AiThority
. Check out the full conversation here: https://
aithority.com/machine-learni
ng/aithority-interview-with-fabrizio-del-maffeo-co-founder-and-ceo-axelera-ai/
… #AI #EdgeComputing #Innovation #TechNews -
Byte Magazine First Issue Published in 1975
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The first issue of the defunct computer magazine Byte was published this month in 1975. v/Small Systems Journal
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Local Execution Eliminates Remote Server Bandwidth Complexity
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If you run locally then none of that complexity is needed – it's describing how to reduce the bandwidth of sending updates to the remove server.
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Skepticism about TD Computing’s Revolutionary Potential and Clarity
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I still don’t know what TD computing really is. Extropic is very vague about it. Faster mcmc sampling by using natural noise? Energy based models based on this mcmc? It does not sound revolutionary to me yet but who knows what rabbit comes out of that hat.
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Building Your GenAI Stack: Infrastructure and Open-Source Strategies
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What should data + AI execs think about as they build their #GenAI stack? Here you go (courtesy of @schwentker
): Evaluate Your AI #Infra – hint: small fine-tuned models can improve efficiency and reduce costs. Invest in Open-Source – #opensource provides greater -
Decentralized Compute Approaches: Finding Learning Resources
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Candidly I don’t have enough thoughts on differences between approaches to decentralized compute. Know of any nice primers, reading, etc?
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Cautious optimism on compute infrastructure and lilypad exploration
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Agree on being cautiously optimistic about compute, but curious nonetheless. Will check lilypad out!
