[deleted]
GPT‑5.3‑Codex‑Spark
141–150 of 415 posts
Re: GPT‑5.3‑Codex‑Spark
#142Earlier quoted context omitted.
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.
> Throughput is ultimately what matters I disagree. Yes it does matter, but because the popular interface is via chat, streaming the results of inference feels better to the squishy messy gross human operating the chat, even if it ends up taking longer. You can give all the benchmark results you want, humans aren't robots. They aren't data driven, they have feelings, and they're going to go with what feels better. Th…
You just have to change the "popular interface" to something else. Chat is OK for trivia or genuinely time-sensitive questions, everything else goes through via email or some sort of webmail-like interface where requests are submitted and replies come back asynchronously. (This is already how batch APIs work, but they only offer a 50% discount compared to interactive, which is not enough to really make a good case for them - especially not for agentic workloads.)
Re: GPT‑5.3‑Codex‑Spark
#143This is a win for agents, speed and intelligence is crucial to the loop. If the time and token cost is small you can iterate many times to correct mistakes. Got to wonder why Wall Street is dumping NVIDIA.
I mean they are only running a small version of codex
can they run the full one? Or the technology isn't there yet?
Re: GPT‑5.3‑Codex‑Spark
#144Interesting to note that the reduced latency is not just due to the improved model speed, but also because of improvements made to the harness itself:
> "As we trained Codex-Spark, it became apparent that model speed was just part of the equation for real-time collaboration—we also needed to reduce latency across the full request-response pipeline. We implemented end-to-end latency improvements in our harness that will benefit all models [...] Through the introduction of a persistent WebSocket connection and targeted optimizations inside of Responses API, we reduced overhead per client/server roundtrip by 80%, per-token overhead by 30%, and time-to-first-token by 50%. The WebSocket path is enabled for Codex-Spark by default and will become the default for all models soon."
I wonder if all other harnesses (Claude Code, OpenCode, Cursor etc.,) can make similar improvements to reduce latency. I've been vibe coding (or doing agentic engineering) with Claude Code a lot for the last few days and I've had some tasks take as long as 30 minutes.
Re: GPT‑5.3‑Codex‑Spark
#145Continue 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
Nvidia seems cooked. Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%). Cerebras will be substantially better for agentic workflows in terms of speed. And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia. And Nvidia won't be cheaper for training new models either.…
Well they `acquired` groq for a reason.
Re: GPT‑5.3‑Codex‑Spark
#146Re: GPT‑5.3‑Codex‑Spark
#147Earlier quoted context omitted.
I love the probabilistic nature of this. Presentations could be anywhere from extremely impressive to hilariously embarrassing.
It would be so cool if it generated live in the presentation and adjusted live as you spoke, so you’d have to react to whatever popped on screen!
Every presentation becomes improv
Re: GPT‑5.3‑Codex‑Spark
#148Earlier quoted context omitted.
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.
By perf I mean how much does it cost to serve 1T model to 1M users at 50 tokens/sec.
All 1T models are not equal. E.g. how many active parameters? what's the native quantization? how long is the max context? Also, it's quite likely that some smaller models in common use are even sub-1T. If your model is light enough, the lower throughput doesn't necessarily hurt you all that much and you can enjoy the lightning-fast speed.
Re: GPT‑5.3‑Codex‑Spark
#149Earlier quoted context omitted.
Nvidia seems cooked. Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%). Cerebras will be substantially better for agentic workflows in terms of speed. And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia. And Nvidia won't be cheaper for training new models either.…
> let every part of the market slip away. Which part of the market has slept away, exactly ? Everything you wrote is supposition and extrapolation. Nvidia has a chokehold on the entire market. All other players still exist in the small pockets that Nvidia doesn’t have enough production capacity to serve. And their dev ecosystem is still so far ahead of anyone else. Which providers gets chosen to equip a 100k chips da…
> Nvidia has a chokehold on the entire market.
You're obviously not looking at expected forward orders for 2026 and 2027.
Re: GPT‑5.3‑Codex‑Spark
#150Earlier quoted context omitted.
Nvidia seems cooked. Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%). Cerebras will be substantially better for agentic workflows in terms of speed. And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia. And Nvidia won't be cheaper for training new models either.…
> What am I missing? Largest production capacity maybe? Also, market demand will be so high that every player's chips will be sold out.
> Largest production capacity maybe?
Anyone can buy TSMC's output...