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.
I need to incorporate "risk of major failure" into bluey bench. Spark is a dangerous model. It doesnt strongly internalize the consequences of the commands that it runs, even on xhigh. As a result I'm observing a high tendency to run destructive commands.
For instance, I asked it to assign random numbers to the filename of the videos in my folder to run the bm. It accidentally deleted the files on most of the runs. The funniest part about it is that it comes back to you within a few seconds and says something like "Whoops, I have to keep it real, I just deleted the files in your folder."
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.
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.
(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.
Read more about how Cerebras hardware handles clustering. The limit is not 44 GB or 500GB. Each CS-3 has 1,200 TB of MemoryX, supporting up to ~24T parameter models. And up to 2,048 can be clustered.
Cerebras doesn't run inference from MemoryX, the same way no other serious inference provider runs inference off of system RAM. MemoryX is connected to the CS-3 over ethernet! It's too slow. MemoryX is only 150GB/sec for the CS-3![1] If you're running inference at 800tokens/sec, with 150GB/sec that means each token can only load 0.18GB of params. For obvious reasons, I don't think OpenAI is using a 0.18B sized model.
The limit is 44GB for each WSE-3. [2] That's how much SRAM a single WSE-3 unit has. For comparison, a Nvidia H100 GPU has 80GB, and a DGX H100 server with 8 GPUs have 640GB of VRAM. Each WSE-3 has 44GB to play around with, and then if you have each one handling a few layers, you can load larger models. That's explicitly what Cerebras says they do: "20B models fit on a single CS-3 while 70B models fit on as few as four systems." [3]
You're reading marketing material drivel about training models that NOBODY uses Cerebras for. Basically nobody uses Cerebras for training, only inference.
[1] https://www.kisacoresearch.com/sites/default/files/documents... "The WSE-2’s 1.2Tb/s of I/O bandwidth is used for [...] transmitting gradients back to the MemoryX service." That quote is about WSE-2/CS-2, but the CS-3 spec lists the same System I/O: 1.2 Tb/s (12×100 GbE).
[2] https://cdn.sanity.io/images/e4qjo92p/production/50dcd45de5a... This really makes it obvious why Cerebras couldn't serve Deepseek R1. Deepseek is 10x larger than a 70b model. Since they don't do tensor parallelism, that means each chip has to wait for the previous one to finish before it can start. So not only is it 10x more memory consumption, it has to load all that sequentially to boot. Cerebras' entire market demands 1000 tokens per second for the much higher price that they charge, so there's no profit in them serving a model which they can only do 500 tokens/sec or something slow like that.
[3] https://www.cerebras.ai/blog/introducing-cerebras-inference-...
This is one area Nvidia chips have not been able to do, ultra fast, ultra high value tasks. Hence, the Grog acquisition.
Cerebras CURRENTLY serves GLM-4.7. I've used it through their API. Look up how big it is. 1,000-1,700 tps. https://www.cerebras.ai/blog/glm-4-7
Not interested in further conversation, so have a nice day! You can go ahead and get in the last word though.