Earlier quoted context omitted.
That's totally nuts. How do they deal with the silicon warping around disabled cores or dark silicon? How long of hard running does it take before the chip gets fatally damaged and needs to be replaced in their system? Word on the street is that h100s fail surprisingly often, this can't be better
https://www.youtube.com/watch?v=7GV_OdqzmIU&t=1104s This video from Cerebras perfectly explain how they solve the interconnect problem, and why their approach greatly reduces the risk of Blackwell-type hardware design challenges.
Cerebras Inference: AI at Instant Speed
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Re: Cerebras Inference: AI at Instant Speed
#42The numbers are pretty incredible. Will the competition be able to match them?
Re: Cerebras Inference: AI at Instant Speed
#43It would be understandable that they are focused currently on inference speed, but features like structured output and prompt caching make it possible to build more capable LLM applications. Does Cerebras support reliable structured output like the recent OpenAI 4o?
For example, I know the latest batch of Mistral models all have json output support.
Re: Cerebras Inference: AI at Instant Speed
#44A single A100 processes at 13 t/s/u for batch 32. That costs $10k to process 39 billion tokens over 3 years = $0.25 tok/s/u. If you have batch size 420 you can do it even cheaper.
TL;DR: Cerebras are certainly advertising at a loss-leading price and will only have a viable product if they can get extraordinarily high utilisation of their system at this price. I don’t think they can, so they’re basically screwed selling tokens. Maybe this is to attract attention in the hope of selling hardware to someone willing to pay a premium for very low latency, but I suspect it’s just a means of getting one more round of funding in the hope of reducing costs in the next version.
Re: Cerebras Inference: AI at Instant Speed
#45Is batched inference for LLMs memory bound? My understanding is that sufficiently large batched matmuls will be compute bound and flash attention has mostly removed the memory bottleneck in the attention computation. If so, the value proposition here -- as well as with other memorymaxxing startups like Groq -- is primarily on the latency side of things. Though my personal impression is that latency isn't really a hug…
If you have an H100 doing 100 tokens/sec and you batch 1000 requests, you might be able to get to 100K tok/sec but each user's request will still be outputting 100 tokens/sec which will make the speed of the response stream the same. So if your output stream speed is slow, batching might not improve user experience, even if you can get a higher chip utilization / "overall" throughput.
Re: Cerebras Inference: AI at Instant Speed
#46Here’s an AI voice assistant I built that uses it:
Re: Cerebras Inference: AI at Instant Speed
#4770b runs on 4x CS-3 estimated at $2-3m each, let’s say total system cost $10m, drawing ~100kW power. They don’t mention batch size, so let’s start with batch size 1 and see where we get. At 100% utilisation for 3 years that’d be 42 billion tokens for a cost of $10m capital plus ~$0.5m power and cooling let’s say, or $250 per million tokens. They’re claiming they can sell their API access at $0.60/million. To break ev…
If this business scales, they can probably afford to lower the price by a factor of 10.
Re: Cerebras Inference: AI at Instant Speed
#48I want to know what the current power requirements are, as well as the cost for the machine. The last time I looked at one of these it was an absolute beast (although very impressive).
https://liquidstack.com/blog/breaking-the-thermal-ceiling-in...
Re: Cerebras Inference: AI at Instant Speed
#49It’s insanely fast. Here’s an AI voice assistant I built that uses it: https://cerebras.vercel.app
Re: Cerebras Inference: AI at Instant Speed
#50Can really fast inference (e.g. 1M tok/sec) make LLMs more intelligent? I am imagining you could run multiple agents and can choose and discard outputs using other LLMs simultaneously. Will the output look more like a real thought process? Or will it remain just same?
It is mentioned in the post: > Traditional LLMs output everything they think immediately, without stopping to consider the best possible answer. New techniques like scaffolding, on the other hand, function like a thoughtful agent who explores different possible solutions before deciding. This “thinking before speaking” approach provides over 10x performance on demanding tasks like code generation, fundamentally boost…