Cerebras CS-4

9cb14c1ec0

Just a reminder for everyone that we are only several years and 3 or 4 iterations into hardware being optimized for LLMs. We should all expect orders of magnitude improvement in speed and/or cost over the next 5 years. Then we can have fun conversations about "unlimited" "intelligence" and about what the price wars and profit margins of consumer AI products are when your average ChatGPT user costs the company $0.10 per month. > CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters Wow!

anonymous_user9

Conspicuously missing: power consumption figures

OutOfHere

Five years from now, I don't know why anyone will still be using Nvidia for inference. Note that Cerebras is for inference only, not for training. I understand that Cerebras has competition, but this bodes even more poorly for Nvidia for inference. Nvidia may still have a role to play for training, however.

4k0hz

> Introducing the all new Cerebras CS-4, a revolutionary rack-scale solution that delivers upto 30x faster inference compared to GPUs, enhanced economics, and a simple path todeploy [sic] hyperscale capacity. Did nobody proofread this?

syntaxing

I think the fun takeaway from this is that GPT 5.4 is probably 45B active parameters and GPT 5.6 Sol is closer to 50B.

gpm

Is it just me or is it bizarre that they're advertising old open-weight models. GLM 4.7 (December 2025) not 5 (Feb) 5.1 (April) or 5.2 (June). 5.3 (4 days ago) is, to be fair, not open weights yet... but there's a lot since 4.7. Kimi K2.7 (April) not K2.7-code (June) or K3 (July). Gemma 4 (April), Llama (April), and gpt-oss (August 2025) are up to date, but old (for models). Meanwhile the closed source GPT 5.6 sol is up to date (June)... Should potential purchasers take away from this that they're not going to be able to run recent models unless they front the cost of developing software or something?

sreekanth850

AMD along with cerebras may probably compete with NVIDIA monopoly in near future. Also, NVIDIA will have competition form multiple companies. Just my prediction.

reilly3000

> CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters Oops did they just out GPT-5.6 sol’s parameter count?

tamimio

I wonder what are the benchmarks of hashcat on different hashes.

lostmsu

KV caching status? What's the point of 1000tok/s if you have to do prefill on every agentic turn which at 100k depth would make it 1.5 min latency every turn?

ethanzhang1024

If cerebars is performing well, why didn't its predecessor, server S-3, become the largest API token provider on OpenRouter, surpassing the official model releases?

aneryu

It would be even better if a version available to individual users were released soon.

sva_

> enabling massive clusters and models with more than 50 trillion parameters

kobe_bryant

can these vibe coded sites please set a max width and overflow so their sites work fine on mobile

avantnyc

Cerebras should slowly also move to dgx/ryzen market for a desktop version for masses at affordable price yet providing substantial tokens/second on desktop

denizay

The comparison seems incomplete. CS‑4 is a full rack-scale system with three wafer-scale processors, but the exact GPU models, GPU count, power consumption, price information are not disclosed. We still don't know if buying a multi-GPU rack (or racks) is cheaper and/or more efficient in power. The fact that they didn't disclose these numbers makes me believe that the numbers are not in their favor. And personally, makes me see them as disingenuous.

adventured

OpenAI needs to immediately move to acquire Cerebras. Nvidia's extreme margin is the opportunity for OpenAI's cost reduction. Buying Cerebras would pay for itself and they should take all of its future production (after filling required contracts). Right now China's models have no silicon moat. Cerebras as a drastic speed-up / cost-reduction potential, can assist in building a competitive moat. And every time a Cerebras pops up, OpenAI or Anthropic should eat them if at all possible. There's no stand-alone frontier AI company of great scale in the near future that doesn't have a large silicon advantage in-house. Apple knew it in smartphones, Google figured it out a long time ago as well.