931–940 of 1001 posts
US labs must be sweating bullets. Not on tech side, but on finance. They have a pile of debt and VC expectations that count on vast future profitability
I switched to exclusively Chinese models, mostly Kimi, many months ago. I'll still ask Claude questions that require ambitious real-time web search / worldly knowledge, but for just about anything else, the Chinese models have been so good that I haven't looked back.
> 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,…
Really feels like end game type stuff - AI designing its own next versions, designing its own chips, etc.. The advancement is slow, but fast - like a plant growing. We really are the boiling frogs now aren’t we? And the people with eyes wide open are us, and anyone that frequents this site really. Is this Milliways?
> 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,…
In comparison, Taalas' implementation (which everyone likes to talk about) is in 6nm, is 815mm^2, and only serves a 8B parameter ~4-bit quant model.
So, 815mm^2 in 6nm is roughly equivalent to ~6112mm^2 in 45nm. If we assume everything scales exactly the same, 4mm^2 would be ~1500x smaller. 1500x smaller means at best we're talking about a ~5M parameter model. I don't know how you'd get a 5M param model (with multiple bits) in 1.46M transistors.
I'm disappointed. After all the buzz and benchmarks, I've tested with my personal benchmark that simulates real-world day-to-day specs for agentic coding, following instructions across long time walls, changing several files and code requirements with separation of concerns to build a complete Saas e2e - it reaches a similar rating as DeepSeek V4 Flash.
And is the benchmark open source?
US labs must be sweating bullets. Not on tech side, but on finance. They have a pile of debt and VC expectations that count on vast future profitability
They'll just lobby the government, feds will label them a security threat and ban them from corporate use. The American AI VC will breathe a huge sigh of relief and call it a day. Nothing says capitalism like writing laws to stop the competition needed to fuel innovation.
Earlier quoted context omitted.
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> I pretty sure OpenAI and Anthropic are doing the same or worse. No they're not. It would end both companies if they were ever found to be doing that. Their terms are clear - if you use the coding plans they can[0] train in return. Enterprise and API, absolutely not. The argument here is that with the Chinese labs you have zero legal recourse. [0] opt-in, thanks