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Cerebras Inference: AI at Instant Speed

cerebras.ai

41–50 of 75 posts

Re: Cerebras Inference: AI at Instant Speed

#41

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.

Super informative video!

Re: Cerebras Inference: AI at Instant Speed

#43
post #13

It 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?

They are running stock Llama 3.x. If the underlying models support structured output, so will they.

For example, I know the latest batch of Mistral models all have json output support.

Re: Cerebras Inference: AI at Instant Speed

#44
70b 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 even on this they’d need a batch size of 420. I don’t know how deep their pipeline is but Llama 3.1 70b has 80 layers with 6 meaningful matmuls per layer so it’s not a crazy multiple of that.

A 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

#45
post #39

Is 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…

Batched inference will increase your overall throughput, but each user will still be seeing the original throughput number. It's not necessarily a memory vs compute issue in the same way training is. It's more a function of the auto-regressive nature of transformer inference as far as I understand which presents unique challenges.

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

#47

70b 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…

your estimate of $2-3M per CS-3 is a price not a cost. It costs about $20K per wafer from TSMC and the price they charge reflects the NRE of designing the system and taping out the masks more than the additional costs to package up their wafers into a system.

If this business scales, they can probably afford to lower the price by a factor of 10.

Re: Cerebras Inference: AI at Instant Speed

#48
post #10

I 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).

> though somewhat astonishingly, the WSE 2 draws 23 kW of power. To put this in perspective, the most powerful GPUs “only” draw around 450W

https://liquidstack.com/blog/breaking-the-thermal-ceiling-in...

Re: Cerebras Inference: AI at Instant Speed

#50
post #31

Can 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…

Is there a tool that provides functionality like this that you can layer on top of cerebras's API, given you are not worried about using 10x-50x more tokens per query.
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