Cerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s

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:

    pipx install llm # or brew install llm or uv tool install llm
    llm install llm-cerebras
    llm keys set cerebras
    # paste key here

    llm -m cerebras-llama3.1-70b 'an epic tail of a walrus pirate'

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?

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.

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...

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...

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).

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

I wonder if there is a token/watt metric. Afaiu cerebras uses plenty of power/cooling.

What made it so much faster based on just a software update?

So what is inference?

Could someone please bring Microsoft's Bitnet into the discussion and explain how its performance relates to this announcement, if at all?

https://github.com/microsoft/BitNet

"bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. "