331–340 of 415 posts
Earlier quoted context omitted.
How did you verify it?
Uhhh, this is my work, so… we didn’t have a SEV? None of our thousands of customers paying us money reported the site was broken?
are there that many use cases for a model that you need code generated as fast as possible rather than better code at decent speeds?
Wow, 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…
Why is the CEO some shady guy? though https://daloopa.com/blog/analyst-pov/cerebras-ipo-red-flags-... "AI" always has some sleazy person behind it for some reason
Earlier quoted context omitted.
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. >…
Uh, the combined offering (smaller model + ~800 tps on cerebras) is nothing like the previous mini offerings, and you're hallucinating details about their process of creating it. 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 interesting for offloading "tiered" workloads / priority queue with coding agents. If 60% of the work is "edit this file with this content", or "refactor according to this abstraction" then low latency - high token inference seems like a needed improvement. Recently someone made a Claude plugin to offload low-priority work to the Anthropic Batch API [1]. Also I expect both Nvidia and Google to deploy custom s…
Note that Batch APIs are significantly higher latency than normal AI agent use. They're mostly intended for bulk work where time constraints are not essential. Also, GPT "Codex" models (and most of the "Pro" models also) are currently not available under OpenAI's own batch API. So you would have to use non-agentic models for these tasks and it's not clear how well they would cope. (Overall, batches do have quite a bi…
For me, it works quite well for low-priority things, without the hassle of using the batch API. Usually the added latency is just a few seconds extra, so it would still work in an agent loop (and you can retry requests that fail at the "normal" priority tier.)
https://developers.openai.com/api/docs/guides/flex-processin...
Earlier quoted context omitted.
Note that Batch APIs are significantly higher latency than normal AI agent use. They're mostly intended for bulk work where time constraints are not essential. Also, GPT "Codex" models (and most of the "Pro" models also) are currently not available under OpenAI's own batch API. So you would have to use non-agentic models for these tasks and it's not clear how well they would cope. (Overall, batches do have quite a bi…
Openai has a "flex" processing tier, which works like the normal API, but where you accept higher latency and higher error rates, in exchange for 50% off (same as batch pricing). It also supports prompt caching for further savings. For me, it works quite well for low-priority things, without the hassle of using the batch API. Usually the added latency is just a few seconds extra, so it would still work in an agent lo…
Wow, 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 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…
for cerbras, can we call them chips? you're no longer breaking the wafer we should call them slabs
Just like potato chips are slices from a potato.
