381–390 of 415 posts
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This is why space is the only acceptable thousands/grouping separator (a non-breaking space when possible). Avoids any confusion.
Space is also confusing! Then it looks like two separate numbers. Underscore (_) is already used as a decimal separator in programming languages and Mathematics should just adopt it, IMO.
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Others clarified the kW versus kWh, but to re-visit the comparison to a household: One household uses about 30 kWh per day. 20 kW * 24 = 480 kWh per day for the server. So you're looking at one server (if parent's 20kW number is accurate - I see other sources saying even 25kW) consuming 16 households worth of energy. For comparison, a hair dryer uses around 1.5 kW of energy, which is just below the rating for most US…
At least with GPT-5.3-Codex-Spark, I gather most of the AI inference isn't rendering cat videos but mostly useful work.. so I don't feel tooo bad about 16 households worth of energy.
1000 tokens per second. Crazy. I'm wondering what this leads to. Imagine the massive amount of software that's going to get built. It will be like reinventing the wheel in a million ways. There will be thousands of alternative internet ecosystems to choose from and each one of then would offer every software system, platform and application that one could possibly need; fully compatible with data transferrable across…
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A competing format to simplify things?
A competing format that is understandable to probably everybody. An ISO 8601 date is also comprehensible to anybody even if they never seen it before and have to figure it out themselves.
Curious how it handles when the speaker goes off-script into something the model has no context for.
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Man I hope someone drinks Nvidia's milk shake. They need to get humbled back to the point where they're desperate to sell gpus to consumers again. Only major road block is cuda...
The nice thing about modern LLMs is that it's a relatively large static use case. The compute is large and expensive enough you can afford to just write custom kernels, to a degree. It's not like CUDA where running on 1, 2, 8 GPUs and you need libraries that already do it all for you, and where researchers are building lots of different models. There aren't all that many different small components between all of the…
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Not for what they are using it for. It is $1m+/chip and they can fit 1 of them in a rack. Rack space in DC's is a premium asset. The density isn't there. AI models need tons of memory (this product annoucement is case in point) and they don't have it, nor do they have a way to get it since they are last in line at the fabs. Their only chance is an aquihire, but nvidia just spent $20b on groq instead. Dead man walking…
How do you know the price of a unit ?
https://www.datacenterdynamics.com/en/news/cerebras-unveils-...
Great move by OpenAI. With coding agents, if you have access to a fast and cheap model, you can afford to let it rip, making lots of mistakes, and iterate until it gets things right. With the right scaffolding (AGENTS.md, SKILLS.md, etc.), a fast and light model can do great things. And when it's done, you can still have the heavyweight model come in to clean up any messes.
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If you run it for an hour, yes.
Ah yes, like those EV chargers that are rated at X kWh/hour.
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Oh don't worry. Ever since the power issue started developing rack space is no longer at a premium. Or at least, it's no longer the limiting factor. Power is.
The dirty secret is that there is plenty of power. But, it isn't all in one place and it is often stranded in DC's that can't do the density needed for AI compute. Training models needs everything in one DC, inference doesn't.