The Ledger
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Feldman: Cerebras is 20 times faster than Nvidia B200 GPUs
“Really focused on performance, both for training and for inference. You think 20 times faster than Nvidia B 200 GPUs and it's been an amazing run.”
Lie: Cerebras chips have 100x more memory than Groq LPUs
“One of our chips has, You know, order a hundred times more memory than one of their chips, right? So you got two orders of magnitude difference in scale kind of for free, right?”
Lie: Cerebras runs OpenAI's flagship model 14x faster than GPUs
“We're running you know, frontier level, one of the most intelligent models, right? OpenAI's largest, most capable, most intelligent model right now at 14 times faster than their normal, you know, GPU speeds.”
Cerebras leads all Artificial Analysis inference benchmarks by a large margin
“I think also just go up and look at artificial analysis. Wherever we are, we're the fastest not by a little bit, but by a lot.”
Feldman: Cerebras provides 2,625x more memory bandwidth than traditional GPUs
“And we have 2625 times more memory bandwidth than the GPU does.”
Cerebras WSE-3 runs Llama inference 70x faster than NVIDIA GPUs
“Cerebris came out that the wafer scale engine three can serve llama 70 B at 2.1 thousand sorry, 202,100 tokens per second and serves llama four or five B at nearly 1000 tokens per second. So this, you know, to give you an understanding, like this is about 70 t…”
Houston: Groq and Cerebras outperform Nvidia on latency
“There's also, like, non-NVIDIA stacks, like the Grok, or Cerebris, or some of these custom silicon companies that are super interesting, and all, and outperformed the NVIDIA stack in terms of latency and things like that.”
Lie: Cerebras demoed GPT running at over 4,400 TPS at Hot Chips
“We here in this demo that we gave at hot chips we're showing GPT OSS running at over 4000 400 TPS, which is just mind blowing.”
Lie: Trillion-parameter models require thousands of Groq LPUs for weights
“To run a frontier level model, like, let's say, a few trillion parameters, you need thousands and thousands of Grok LPUs just to hold the weights, right?”
Feldman: Cerebras raised $1.1B at an $8.1B valuation
“So we announced a 1.1 billion dollar fundraise that we had completed. It was done at an 8.1 billion dollar post money valuation, and it was led by Fidelity and Atreides management.”
No competing AI framework disaggregates model storage from compute like Cerebras
“Like basically not, no one is doing anything close to where you're disaggregating. Model storage from compute. And none of these examples above do that either.”
GPUs cannot handle unstructured sparsity as efficiently as Cerebras hardware
“So both cerebris and GPUs can handle structured sparsity But GPUs are not designed to handle unstructured sparsity, whereas what I've just mentioned before is able to handle this unstructured sparsity.”
Lie: Cerebras CS-4 doubles wafer power and bandwidth while halving latency
“We've designed this a modular platform that provides twice the amount of power to the wafer than we have in our previous generation. Twice the amount of interconnect bandwidth, half the latency.”
Feldman: Sam Altman and Ilya Sutskever invested in Cerebras' early rounds
“In 2016, we met with Sam Altman and Ilya Suskovard at OpenAI and they were an idea and we were PowerPoint, right? That's amazing. And what AI was doing was identifying cats in pictures. And I think they ended up investing in us, both of them and many of their …”
Cerebras weight streaming prunes up to 90% of data without accuracy loss
“And so memory, and so as MemoryX streams weights through SwarmX, it eliminates zero and near zero values, and so this reduces bandwidth requirements significantly, pruning up to 90% of the data while maintaining accuracy.”
Cerebras WSE-3 per-core SRAM eliminates central memory bandwidth bottlenecks
“So what Cerebrus has done for the wafer scale engine three is that instead of storing all these weights and values, weights and values off chip, Cerebrus stores everything on chip in SRAM. So every single one of the cores on the wafer scale engine three has it…”
Cerebras MemoryX scales to 2.4 petabytes to support 120-trillion-parameter AI models
“And you know, it scales from four terabytes to 2.4 petabytes, Supports models with up to 120 trillion parameters and then it utilizes DRAM and flash storage.”
Cerebras WSE-3 features 900,000 cores, 44GB SRAM, and 4 trillion transistors
“And so the wafer scale engine three, as I mentioned, 900,000 cores, 44 gigabytes of SRAM, four trillion transistors, and I do add a note here that the paper focuses on wafer scale engine two, and so the wafer scale engine three is, you know, just an upgraded v…”
Cerebras weight streaming is exclusively for training, while inference runs on SRAM
“The memory X and swarm X, this whole waste streaming system is just used for is just used for training. So for inference, you just using the SRAM, you know, at 44 gigabytes on the chip, and then you can network multiple chips together to support larger models.”
Cerebras streams weights from MemoryX and computes updates externally
“And instead of storing all the weights that the compute units need on
[737] Sarah Chieng: On the compute unit, it's storing it externally in an external memory service. In this case, it's called memory X. And during training, these weights are streamed from me…”