It feels oddly freeing to be seeing headlines like this every other day on HN and not caring in the slightest. The titles are just amalgamations of random words to me, like 'Claude Super Zen Deep 4.0' or 'Grok Hyper 6.2 Mega'. They come and go. A month from now it'll be new headlines with new numbers and new words. And I still won't care. Not in the rat race, just using whatever chatgpt gives me for free. Just coding how I've always coded.
GPT‑5.3‑Codex‑Spark
311–320 of 415 posts
Re: GPT‑5.3‑Codex‑Spark
#312It feels oddly freeing to be seeing headlines like this every other day on HN and not caring in the slightest. The titles are just amalgamations of random words to me, like 'Claude Super Zen Deep 4.0' or 'Grok Hyper 6.2 Mega'. They come and go. A month from now it'll be new headlines with new numbers and new words. And I still won't care. Not in the rat race, just using whatever chatgpt gives me for free. Just coding…
Sign of time, this resembles time when we were moving ahead with processors speed. Two y.o. computer was obsolete (or, in those times, it required an upgrade, as it was possible...).
Re: GPT‑5.3‑Codex‑Spark
#313Earlier quoted context omitted.
There have been discussions about this chip here in the past. Maybe not that particular one but previous versions of it. The whole server if I remember correctly eats some 20KWs of power.
A first-gen Oxide Computer rack puts out max 15 kW of power, and they manage to do that with air cooling. The liquid-cooled AI racks being used today for training and inference workloads almost certainly have far higher power output than that. (Bringing liquid cooling to the racks likely has to be one of the biggest challenges with this whole new HPC/AI datacenter infrastructure, so the fact that an aircooled rack ca…
Well for some. Google has been using liquid cooling to racks for decades.
Re: GPT‑5.3‑Codex‑Spark
#314[dead]
Re: GPT‑5.3‑Codex‑Spark
#315Earlier quoted context omitted.
Yea it's been butchering relatively easy to moderate tasks for me even with reasoning set to high. I am hoping it's just tuning that needs to be done since they've had to port it to a novel architecture. If instead the model is performing worse due to how much they had to shrink it just so it will fit on Cerebras hardware, then we might be in for a long wait for the next gen of ginormous chips.
> If instead the model is performing worse due to how much they had to shrink it just so it will fit on Cerebras hardware They really should have just named it "gpt-5.3-codex-mini" (served by Cerebras). It would have made it clear what this model really is.
Not if you're suggesting that "(served by Cerebras)" should be part of the name. They're partnering with Cerebras and providing a layer of value. Also, OpenAI is "serving" you the model.
We don't know how they integrate with Cerebras hardware, but typically you'd pay a few million dollars to get the hardware in your own datacenter. So no, "served by Cerebras" is confusing and misleading.
Also "mini" is confusing because it's not analagous to gpt-5.1-codex vs gpt-5.1-codex-mini. Gpt-5.3-codex-spark is a unique, _experimental_ offering that doesn't fit the existing naming suffixes.
I don't understand what's wrong with "spark". It's friendly and evokes a sense of something novel, which is perfect.
If you want to know more about the model, read the first paragraph of the article. That information doesn't need to be hardcoded into the model name indefinitely. I don't see any "gpt-5.3-codex-nvidia" models.
Re: GPT‑5.3‑Codex‑Spark
#316Wow, I wish we could post pictures to HN. That chip is HUGE!!!! The WSE-3 is the largest AI chip ever built, measuring 46,255 mm² and containing 4 trillion transistors. It delivers 125 petaflops of AI compute through 900,000 AI-optimized cores — 19× more transistors and 28× more compute than the NVIDIA B200. From https://www.cerebras.ai/chip : https://cdn.sanity.io/images/e4qjo92p/production/78c94c67be9... https://cd…
Wow, I'm staggered, thanks for sharing I was under the impression that often times chip manufacture at the top of the lines failed to be manufactured perfectly to spec and those with say, a core that was a bit under spec or which were missing a core would be down clocked or whatever and sold as the next in line chip. Is that not a thing anymore? Or would a chip like this maybe be so specialized that you'd use say a g…
IIRC, a lot of design went into making it so that you can disable parts of this chip selectively.
Re: GPT‑5.3‑Codex‑Spark
#317Earlier quoted context omitted.
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…
The real question is what’s their perf/dollar vs nvidia?
That's coupling two different usecases.
Many coding usecases care about tokens/second, not tokens/dollar.
Re: GPT‑5.3‑Codex‑Spark
#318Continue to believe that Cerebras is one of the most underrated companies of our time. It's a dinner-plate sized chip. It actually works. It's actually much faster than anything else for real workloads. Amazing
I'm fascinated by how the economy is catching up to demand for inference. The vast majority of today's capacity comes from silicon that merely happens to be good at inference, and it's clear that there's a lot of room for innovation when you design silicon for inference from the ground up.
With CapEx going crazy, I wonder where costs will stabilize and what OpEx will look like once these initial investments are paid back (or go bust). The common consensus seems to be that there will be a rug pull and frontier model inference costs will spike, but I'm not entirely convinced.
I suspect it largely comes down to how much more efficient custom silicon is compared to GPUs, as well as how accurately the supply chain is able to predict future demand relative to future efficiency gains. To me, it is not at all obvious what will happen. I don't see any reason why a rug pull is any more or less likely than today's supply chain over-estimating tomorrow's capacity needs, and creating a hardware (and maybe energy) surplus in 5-10 years.
Re: GPT‑5.3‑Codex‑Spark
#319Wow, I wish we could post pictures to HN. That chip is HUGE!!!! The WSE-3 is the largest AI chip ever built, measuring 46,255 mm² and containing 4 trillion transistors. It delivers 125 petaflops of AI compute through 900,000 AI-optimized cores — 19× more transistors and 28× more compute than the NVIDIA B200. From https://www.cerebras.ai/chip : https://cdn.sanity.io/images/e4qjo92p/production/78c94c67be9... https://cd…
> 46,255 mm² To be clear: that's the thousandths separator, not the Nordic decimal. It's the size of a cat, not the size of a thumbnail.
*thousands, not thousandths, right?
The correct number is fourty six thousand, two hundred and fifty five square mm.
Re: GPT‑5.3‑Codex‑Spark
#320Earlier quoted context omitted.
> If instead the model is performing worse due to how much they had to shrink it just so it will fit on Cerebras hardware They really should have just named it "gpt-5.3-codex-mini" (served by Cerebras). It would have made it clear what this model really is.
Not if you're suggesting that "(served by Cerebras)" should be part of the name. They're partnering with Cerebras and providing a layer of value. Also, OpenAI is "serving" you the model. We don't know how they integrate with Cerebras hardware, but typically you'd pay a few million dollars to get the hardware in your own datacenter. So no, "served by Cerebras" is confusing and misleading. Also "mini" is confusing beca…
Uh, that paragraph translated from "marketing bullshit" into "engineer" would be "we distilled the big gpt-5.3-codex model into a smaller size that fits on the 44GB of SRAM of a Cerebras WSE-3 multiplied by whatever tensor parallel or layer parallel grouping they're doing".
(Cerebras runs llama-3.3 70b on 4 WSE-3 units with layer parallelism, for example).
That's basically exactly what gpt-5.3-codex-mini would be.
> Also "mini" is confusing because it's not analagous to gpt-5.1-codex vs gpt-5.1-codex-mini.
So perhaps OpenAI intentionally picked the model's layer param count, MoE expert size, etc to fit onto the Cerebras machines. That's like saying "the DVD producer optimized this movie for you" (they just cropped and compressed it down to 4.7GB so it would fit on a DVD). Maybe the typical mini model is 100gb, and they made it 99gb instead or something like that. It's still analogous to gpt-5.3-codex-mini.
I'm underselling it a little bit, because it takes a bit more work than that to get models to run on Cerebras hardware (because they're so weird and un-GPU-like), but honestly if Cerebras can get Llama 3.1 405b or GLM 4.7 running on their own chips, it's not that much harder to have Cerebras get gpt-5.3-codex-mini running.