DeepSeek launched a free, open-source large language model in late December, claiming it was developed in just two months at a cost of under $6 million.
I’m not making an argument against it, just clarifying were it sits as technology.
As I see it, it’s like electric cars - a technology that was overtaken by something else in the early days when that domain was starting even though it was the first to come out (the first cars were electric and the ICE engine was invented later) and which has now a chance to be successful again because many other things have changed in the meanwhile and we’re a lot closes to the limits of the tech that did got widely adopted back in the early days.
It actually makes a lot of sense to improve the speed of what programming can do by getting it to be capable of also work outside the step-by-step instruction execution straight-jacked which is the CPU/GPU clock.
Yes, but I’m not sure what your argument is here.
Least resistance to an outcome (in this case whatever you program it to do) is faster.
Applicable to waterfall flows, FPGA makes absolute sense for the neural networks as they operate now.
I’m confused on your argument against this and why GPU is better. The benchmarks are out in the world, go look them up.
I’m not making an argument against it, just clarifying were it sits as technology.
As I see it, it’s like electric cars - a technology that was overtaken by something else in the early days when that domain was starting even though it was the first to come out (the first cars were electric and the ICE engine was invented later) and which has now a chance to be successful again because many other things have changed in the meanwhile and we’re a lot closes to the limits of the tech that did got widely adopted back in the early days.
It actually makes a lot of sense to improve the speed of what programming can do by getting it to be capable of also work outside the step-by-step instruction execution straight-jacked which is the CPU/GPU clock.