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GPT‑5.3‑Codex‑Spark

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Re: GPT‑5.3‑Codex‑Spark

#91

Post by latchkey »

p1esk wrote: post #89
latchkey wrote: post #84

Earlier 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?

Exactly. They won't ever tell you. It is never published.

Let's not forget that the CEO is an SEC felon who got caught trying to pull a fast one.

Re: GPT‑5.3‑Codex‑Spark

#92

Post by xnx »

pjs_ wrote: post #66

Continue 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

Cerebras is a bit of a stunt like "datacenters in spaaaaace".

Terrible yield: one defect can ruin a whole wafer instead of just a chip region. Poor perf./cost (see above). Difficult to program. Little space for RAM.

Re: GPT‑5.3‑Codex‑Spark

#94

Post by simonw »

basilgohar wrote: post #52
simonw wrote: post #43

Earlier quoted context omitted.

How hard have you tried? I've been finding that the Opus 4.5/4.6 and GPT-5.2/5.3 models really have represented a step-change in how good they are at running long tasks. I can one-shot prompt all sorts of useful coding challenges now that previously I would have expected to need multiple follow-ups to fix mistakes the agents made. I got all of this from a single prompt, for example: https://github.com/simonw/research…

Can you share any examples of these one-shot prompts? I've not gotten to the point where I can get those kind of results yet.

If you look through the commit logs on simonw/research and simonw/tools on GitHub most commits should either list the prompt, link to a PR with the prompt or link to a session transcript.

Re: GPT‑5.3‑Codex‑Spark

#95

Post by p1esk »

nikkwong wrote: post #64
gamegoblin wrote: post #38

Earlier quoted context omitted.

I routinely leave codex running for a few hours overnight to debug stuff If you have a deterministic unit test that can reproduce the bug through your app front door, but you have no idea how the bug is actually happening, having a coding agent just grind through the slog of sticking debug prints everywhere, testing hypotheses, etc — it's an ideal usecase

I have a hard time understanding how that would work — for me, I typically interface with coding agents through cursor. The flow is like this: ask it something -> it works for a min or two -> I have to verify and fix by asking it again; etc. until we're at a happy place with the code. How do you get it to stop from going down a bad path and never pulling itself out of it? The important role for me, as a SWE, in the p…

“here's a failing test—do whatever you can to fix it”

Bad idea. It can modify the code that the test passes but everything else is now broken.

Re: GPT‑5.3‑Codex‑Spark

#96

Post by spwa4 »

latchkey wrote: post #84
pjs_ wrote: post #66

Continue 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

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…

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.

Re: GPT‑5.3‑Codex‑Spark

#97

Post by the_duke »

xnx wrote: post #92
pjs_ wrote: post #66

Continue 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

Cerebras is a bit of a stunt like "datacenters in spaaaaace". Terrible yield: one defect can ruin a whole wafer instead of just a chip region. Poor perf./cost (see above). Difficult to program. Little space for RAM.

They claim the opposite, though, saying the chip is designed to tolerate many defects and work around them.

Re: GPT‑5.3‑Codex‑Spark

#98

Post by zozbot234 »

p1esk wrote: post #89
latchkey wrote: post #84

Earlier 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?

I guess it depends what you mean by "perf". If you optimize everything for the absolutely lowest latency given your power budget, your throughput is going to suck - and vice versa. Throughput is ultimately what matters when everything about AI is so clearly power-constrained, latency is a distraction. So TPU-like custom chips are likely the better choice.

Re: GPT‑5.3‑Codex‑Spark

#99

Post by latchkey »

spwa4 wrote: post #96
latchkey wrote: post #84

Earlier 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…

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.

Re: GPT‑5.3‑Codex‑Spark

#100

Post by mynti »

With the rough numbers from the blog post at ~1k tokens a second in Cerebras it should put it right at the same size as GLM 4.7, which also is available at 1k tokens a second. And they say that it is a smaller model than the normal Codex model
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