Yes. https://inferencex.semianalysis.com provides some comparisons wrt. certain cost metrics. Some commonly used ones are vLLM, TensorRT-LLM, and SGLang. These three at least are open source, and all come with an Apache 2.0 license.
Some providers also have to implement their own engines, e.g., Cerebras has their own inference serving stack for their wafer-scale chips, as does Google for their TPUs (XLA compiler).
Just that an actual rubber duck doesn’t do anything. You solve the problem you have by talking, and in doing so, thinking, to come up with a solution, an idea, or gain better understanding.
After that you either implement something yourself or have learned something.
With an LLM you offload all of that, the only thing you still do is tell it what the problem is. The agentic duck does the rest and you look at the output.
Even if you have to argue, you argue without having gone through the steps to gain anything yourself.
I basically spend way to much time with RISC-V related things.
But the easiest way to get more info about RISC-V developmemts is by watching the youtube uploads of the RISC-V summit talks.
Every SemiCon usually has a RiscV forum. Obviously there are also RiscV events. Wesemibay usually has several RiscV processors and GPU's from new vendors and you can then go and check what features they support. But basically as with everything else you have to show up and talk to people, or just look at the summaries afterwards.
You'd need hundreds of GB alone for the KV cache of each user. For something like LLama 3 405B you need ~67GB at ~130k tokens.
A single CS-3 has 44GB on-chip sram.
So, afaik, Cerebras are optimizing for ultra-low latency batch=1 inference.
Congrats on the great job with the Altar II. If I didn't already have too many keyboards, I'd hop on the Kickstarter!
FWIW, I fully agree with your opinion on including a `half working` fingerprint sensor.
if you assume that training requires about 3x the compute of inference (one forward pass, one backward pass, parameter updates), and we take DeepSeek-V3 since their numbers are public.
they used ~14.8 trillion tokens with about 2.66 million GPU hours.
14.8 * 3 = 44.4 t inference tokens.
obviously, this is back of the envelope math, but at 100t/s you would need like ~14k years. scale this to >100k GPUs and your in the hours to a couple days range.
Some providers also have to implement their own engines, e.g., Cerebras has their own inference serving stack for their wafer-scale chips, as does Google for their TPUs (XLA compiler).