601–610 of 1001 posts
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I wouldn't be surprised if models were optimizing for rendering SVG pelicans at this point
every ai release thread seems to have this same sequence of comments
“Open” frontier intelligence? In what sense? Have they announced their intention to release the weights similar to their previous releases?
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These benchmark numbers are insane. The days when China was 6 months behind are over? How are they doing this with so much less resources than the US??? I have so much respect for the researchers there
Mythos/Fable-class models have been around for at least 4 months internally in the US, and Kimi still isn't quite there, so I'd say the 6-months is still about right.
“Open” frontier intelligence? In what sense? Have they announced their intention to release the weights similar to their previous releases?
It's my dream to own hardware that can run this!
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These benchmark numbers are insane. The days when China was 6 months behind are over? How are they doing this with so much less resources than the US??? I have so much respect for the researchers there
I'm not sure where "so much less resources" comes from. Training the best model has nothing to do with having the most NVIDIA GPUs around. If that were true then xAI would have the best model. It comes down to the quality of data, research, and financial backing.
Pelican: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... - rendered via the OpenRouter API: https://openrouter.ai/moonshotai/kimi-k3 95 input, 16,658 output = 25 cents! https://www.llm-prices.com/#it=95&ot=16658&ic=3&oc=15 (13,241 of those were reasoning tokens.) I think that's the most expensive pelican I've rendered through a Chinese model so far.
I wouldn't be surprised if models were optimizing for rendering SVG pelicans at this point
> Chip Design > As an early proof of concept, Kimi K3 designed a chip to serve a nano model built on its own architecture. In a single 48-hour autonomous run, K3 built, optimized, and verified the chip using open-source EDA tools on the Nangate 45nm library. Within 4 mm², the chip closes timing at 100 MHz and sustains over 8,700 tokens/s decode throughput in simulation, packing 1.46M standard cells, 0.277 MB of SRAM,…
I had a thought a while back: sell large local models burned onto fused compute / ROM chips. Like cartridges for old game consoles. Slot (or probably plug into USB-C) and go. It’s an ASIC with the model wired into it so it’s very low power and fast. I’d buy these. Say $100 for a frontier class model. Maybe more.