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Earlier quoted context omitted.
I have it on good authority Apple was very closing to acquiring Groq
If this is true, expect a call from the SEC...
[1] https://www.sec.gov/education/capitalraising/building-blocks...
But the "completely novel silicon architecture" and the "self-developed LPU" (claiming not to use GPUs)... makes me bit skeptical. After all, pure speed might be achievable through stacking computational power and model quantization. Shouldn't innovation at the GPU level be quite challenging, especially to achieve such groundbreaking speeds?
For me, it was fast when it started printing (it did almost instantly), but it took forever for it to start.
Earlier quoted context omitted.
I will mention: A lot of innovation in this space comes bottom-up. The sooner you can get something in the hands of individuals and smaller institutions, the better your market position will be. I'm coding to NVidia right now. That builds them a moat. The instant I can get other hardware working, the less of a moat they will have. The more open it is, the more likely I am to adopt it.
Definitely, that's why we've opened our API to everyone.
Best-case would be something I buy for (The mistake a lot of services make is to limit free tier to e.g. 30 day or 1 year, rather than hours/month; if I didn't get around to evaluating, switch employers, switch projects, etc. the free tier is gone).
I did sign up for your API service. I won't be able to use it in prod before your (very nice) privacy guarantees are turned into lawyer-compliant regulatory language. But it's an almost ideal fit for my application.
The Groq demo was indeed impressive. I work with LLM alot in work, and a generation speed of 500+ tokens/s would definitely change how we use these products. (Especially considering it's an early-stage product) But the "completely novel silicon architecture" and the "self-developed LPU" (claiming not to use GPUs)... makes me bit skeptical. After all, pure speed might be achievable through stacking computational power…
price and speed benchmark: https://wow.groq.com/
The main problem with the Groq LPUs is, they don't have any HBM on them at all. Just a miniscule (230 MiB) [0] amount of ultra-fast SRAM (20x faster than HBM3, just to be clear). Which means you need ~256 LPUs (4 full server racks of compute, each unit on the rack contains 8x LPUs and there are 8x of those units on a single rack) just to serve a single model [1] where as you can get a single H200 (1/256 of the server…
Groq Engineer here, I'm not seeing why being able to scale compute outside of a single card/node is somehow a problem. My preferred analogy is to a car factory: Yes, you could build a car with say only one or two drills, but a modern automated factory has hundreds of drills! With a single drill, you could probably build all sorts of cars, but a factory assembly line is only able to make specific cars in that configur…
I built one, should be live soon ;-)
Very impressive looking! Just wanted to caution it's worth being a bit skeptical without benchmarks as there are a number of ways to cut corners. One prominent example is heavy model quantization, which speeds up the model at a cost of model quality. Otherwise I'd love to see LLM tok/s progress exactly like CPU instructions/s did a few decades ago.
Link to benchmarking: https://artificialanalysis.ai/ (Note question was regarding API rather than their chat demo)
The main problem with the Groq LPUs is, they don't have any HBM on them at all. Just a miniscule (230 MiB) [0] amount of ultra-fast SRAM (20x faster than HBM3, just to be clear). Which means you need ~256 LPUs (4 full server racks of compute, each unit on the rack contains 8x LPUs and there are 8x of those units on a single rack) just to serve a single model [1] where as you can get a single H200 (1/256 of the server…
Groq Engineer here, I'm not seeing why being able to scale compute outside of a single card/node is somehow a problem. My preferred analogy is to a car factory: Yes, you could build a car with say only one or two drills, but a modern automated factory has hundreds of drills! With a single drill, you could probably build all sorts of cars, but a factory assembly line is only able to make specific cars in that configur…
I believe that this is doable - my pipeline is generally closer to 400ms without RAG and with Mixtral, with a lot of non-ML hacks to get there. It would also definitely be doable with a joint speech-language model that removes the transcription step.
For these use cases, time to first byte is the most important metric, not total throughput.