Cerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s
86 comments
·October 25, 2024simonw
croes
It has a waiting list
londons_explore
The "AI overview" in google search seems to be a similar speed, and the resulting text of similar quality.
simonw
I wonder which of their models they use. Might even be Gemini 1.5 Flash 8B which is VERY quick.
I just tried that out with the same prompt and it's fast, but not as fast as Cerebras: https://static.simonwillison.net/static/2024/gemini-flash-8b...
londons_explore
I suspect it is its own model. Running it on 10B+ user queries per day you're gonna want to optimize everything you can about it - so you'd want something really optimized to the exact problem rather than using a general purpose model with careful prompting.
obviyus
Wonder if they'll eventually release Whisper support. Groq has been great for transcribing 1hr+ calls at a significnatly lower price compared to OpenAI ($0.36/hr vs. $0.04/hr).
Arn_Thor
Whisper runs so well locally on any hardware I’ve thrown at it, why run it in the cloud?
swores
Does it run well on CPU? I've used it locally but only with my high end (consumer/gaming) GPU, and haven't got round to finding out how it does on weaker machines.
Arn_Thor
It’s not fast but if your transcript doesn’t have to get out ASAP it’s fine
obviyus
That's pretty much exactly how I started. Ran whisper.cpp locally for a while on a 3070Ti. It worked quite well when n=1.
For our use case, we may get 1 audio file at a time, we may get 10. Of course queuing them is possible but we decided to prioritize speed & reliability over self hosting.
Arn_Thor
Got it. Makes sense in that context
BrunoJo
https://Lemonfox.ai is another alternative to OpenAI's Whisper API if you need support for word-level timestamps and diarization.
maz1b
Cerebras really has impressed me with their technicality and their approach in the modern LLM era. I hope they do well, as I've heard they are en-route to IPO. It will be interesting to see if they can make a dent vs NVIDIA and other players in this space.
madaxe_again
Apparently so. You can also buy in via various PE outfits before IPO, if you so desire. I did.
Max-20
Which one did you use? I am also interested to do that.
GavCo
When Meta releases the quantized 70B it will give another > 2X speedup with similar accuracy: https://ai.meta.com/blog/meta-llama-quantized-lightweight-mo...
YetAnotherNick
You don't need quantization aware training on larger models. 4 bit 70b and 405b models exhibit close to zero degradation in output with post training quantization[1][2].
[1]: https://arxiv.org/pdf/2409.11055v1 [2]: https://lmarena.ai/
WanderPanda
I wonder why that is? because they are trained with dropout?
david-gpu
Probably because of how bloody large they are. The quantization errors likely cancel each other out over the sum of so many terms.
Same reason why you can get a pretty good reconstruction when you add random noise to an image and then apply a binary threshold function to it. The more pixels there are, the more recognizable will be the B&W reconstruction.
ipsum2
Probably not. Cerebras chip only has 16bit and 32bit operators.
asabla
Damn, that's some impressive speeds.
At that rate it doesn't matter if the first try resulted in an unwanted answer, you'll be able to run once or twice more in a fast succession.
I hope their hardware stays relevant as this field continues to evolve
tjoff
The biggest time sink for me is validating answers so not sure I agree on that take.
Fast iteration is a killer feature, for sure, but at this time I'd rather focus on quality for it to be worthwhile the effort.
vineyardmike
If you're using an LLM as a compressed version of a search index, you'll be constantly fighting hallucinations. Respectfully, you're not thinking big-picture enough.
There are LLMs today that are amazing at coding, and when you allow it to iterate (eg. respond to compiler errors), the quality is pretty impressive. If you can run an LLM 3x faster, you can enable a much bigger feedback loop in the same period of time.
There are efforts to enable LLMs to "think" by using Chain-of-thought, where the LLM writes out reasoning in a "proof" style list of steps. Sometimes, like with a person, they'd reach a dead-end logic wise. If you can run 3x faster, you can start to run the "thought chain" as more of a "tree" where the logic is critiqued and adapted, and where many different solutions can be tried. This can all happen in parallel (well, each sub-branch).
Then there are "agent" use cases, where an LLM has to take actions on its own in response to real-world situations. Speed really impacts user-perception of quality.
phito
> There are LLMs today that are amazing at coding, and when you allow it to iterate (eg. respond to compiler errors), the quality is pretty impressive. If you can run an LLM 3x faster, you can enable a much bigger feedback loop in the same period of time.
Well now the compiler is the bottleneck isn't it? And you would still need human check for bugs that aren't caught by the compiler.
Still nice to have inference speed improvements tho.
tjoff
If the speed is used to get better quality with no more input from the user then sure, that is great. But that is not the only way to get better quality (though I agree that there are some low hanging fruit in the area).
OhNoNotAgain_99
To be honest most LLM's are reasonable at coding, they're not great. Sure they can code small stuff. But the can't refactor large software projects, or upgrade them.
jeswin
> The biggest time sink for me is validating answers so not sure I agree on that take.
But you're assuming that it'll always ne validated by humans. I'd imagine that most validation (and subsequent processing, especially going forward) will be done on machines.
tjoff
If that is the way to get quality, sure.
Otherwise I feel that power consumption is the bigger issue than speed, though in this case they are interlinked.
yunohn
How does the next machine/LLM know what’s valid or not? I don’t really understand the idea behind layers of hallucinating LLMs.
croes
And who validates the validation?
croes
Exactly, validating and rewriting the prompt are the real time consuming tasks.
d4rkp4ttern
For those looking to easily build on top of this or other OpenAI-compatible LLM APIs -- you can have a look at Langroid[1] (I am the lead dev): you can easily switch to cerebras (or groq, or other LLMs/Providers). E.g. after installing langroid in your virtual env, and setting up CEREBRAS_API_KEY in your env or .env file, you can run a simple chat example[2] like this:
python3 examples/basic/chat.py -m cerebras/llama3.1-70b
import langroid.language_models as lm
import langroid as lr
llm_config = lm.OpenAIGPTConfig(chat_model= "cerebras/llama3.1-70b")
agent = lr.ChatAgent(
lr.ChatAgentConfig(llm=llm_config, system_message="Be helpful but concise"))
)
task = lr.Task(agent)
task.run()
fancyfredbot
Wow, software is hard! Imagine an entire company working to build an insanely huge and expensive wafer scale chip and your super smart and highly motivated machine learning engineers get 1/3 of peak performance on their first attempt. When people say NVIDIA has no moat I'm going to remember this - partly because it does show that they do, and partly because it shows that with time the moat can probably be crossed...
a2128
I wonder at what point does increasing LLM throughput only start to serve negative uses of AI. This is already 2 orders of magnitude faster than humans can read. Are there any significant legitimate uses beyond just spamming AI-generated SEO articles and fake Amazon books more quickly and cheaply?
Workaccount2
The way things are going it looks like tokens/s is going to play a big role. O1 preview devours tokens and now Anthropic computer use is devouring them too. Video generation is extremely token heavy too.
It sort of is starting to look like you can linearly boost utility by exponentially scaling token usage per query. If so we might see companies slowing on scaling parameters and instead focusing on scaling token usage.
adwn
How about just serving more clients in parallel? I don't see why human reading-speed should pose any kind of upper bound.
And then there are use cases like OpenAI's o1, where most tokens aren't even generated for the benefit of a human, but as input for itself.
odo1242
What made it so much faster based on just a software update?
anon291
Ex-cereberas engineer here. The chip is very powerful and there is no 'one way' to do things. Rearchitecting data flow, changing up data layout, etc can lead to significant performance improvements. That's just my informed speculation. There's likely more perf somewhere
campers
The first implementation of inference on the Wafer Scale Engine and utilized only a fraction of its peak bandwidth, compute, and IO capacity. Today’s release is the culmination of numerous software, hardware, and ML improvements we made to our stack to greatly improve the utilization and real-world performance of Cerebras Inference.
We’ve re-written or optimized the most critical kernels such as MatMul, reduce/broadcast, element wise ops, and activations. Wafer IO has been streamlined to run asynchronously from compute. This release also implements speculative decoding, a widely used technique that uses a small model and large model in tandem to generate answers faster.germanjoey
They said in the announcement that they've implemented speculative decoding, so that might have a lot to do with it.
A big question is what they're using as their draft model; there's ways to do it losslessly, but they could also choose to trade off accuracy for a bigger increase in speed.
It seems they also support only a very short sequence length. (1k tokens)
bubblethink
Speculative decoding does not trade off accuracy. You reject the speculated tokens if the original model does not accept them, kind of like branch prediction. All these providers and third parties benchmark each other's solutions, so if there is a drop in accuracy, someone will report it. Their sequence length is 8k.
majke
I wonder if there is a token/watt metric. Afaiu cerebras uses plenty of power/cooling.
accrual
I found this on their product page, though just for peak power:
> At 16 RU, and peak sustained system power of 23kW, the CS-3 packs the performance of a room full of servers into a single unit the size of a dorm room mini-fridge.
It's pretty impressive looking hardware.
menaerus
Weighing 800kg (!). Like, what the heck.
neals
So what is inference?
jonplackett
Inference just means using the model, rather than training it.
As far as I know Nvidia still has a monopoly on the training part.
AIFounder
[dead]
It turns out someone has written a plugin for my LLM CLI tool already: https://github.com/irthomasthomas/llm-cerebras
You need an API key - I got one from https://cloud.cerebras.ai/ but I'm not sure if there's a waiting list at the moment - then you can do this:
Then you can run lightning fast prompts like this: Here's a video of that running, it's very speedy: https://static.simonwillison.net/static/2024/cerebras-is-fas...