answers just with zero shot prompting for a given task. We've also done lots and lots of work to optimize the memory footprint of that two billion parameter model as much as we possibly can. Um, so it uses a mix of like two bit, four bit and 8 bit quantization getting it down to like I know like 2.9 bits per weight if you look at the actual weights we need to hold in memory. We do other tricks like per layer embeddings. I won't go into like all of the detail here, but end result is we can you know you need maybe one like here it's 841 megabytes for a texton model in memory just for the weights and then you know maybe by the time you add in the runtime a KV cache um footprint you you might be up to requiring like two gigs of active RAM to be able to run this model then you account for an OS and the fact that there's other things going on that's where we get the kind of four four gig plus rule of thumb uh for deploying this on a device. Um then in terms of speed, this is using our runtime. This is just a list of devices that we run on. Uh for the purpose of this talk, we're going to look more closely at the last three rows of the table, which is if we take that two billion parameter model and run it on a Raspberry Pi. Um that is will give about 7.6 tokens per second decode. This is without MTP. If you turn on MTP, that'll get maybe 2x faster depending on the task. Um, if you go to a higher a more capable device like a Jetson RN Nano, we can get up to maybe 24 um tokens per second decode or maybe even faster if you use Nvidia's own tool chain. This is with our tool chain. Um we also have done work to port this to a Qualcomm IoT board which is pretty popular among kind of higherend uh robotics and IoT applications and there yeah you can see you can get about like almost 4,000 tokens per second uh preill uh 31 tokens per second decode and that's useful for lots of like almost real-time um uh applications uh on an NPU because um with with these uh with Gemma 4 models like One medium resolution image is like kind of 500 tokens. A high resolution image is 1120 tokens. So you could get like you know three frames per second of high resol high resolution tokens um going through this model and have pretty decent decode speed as well. So there's lots and lots of compelling applications you can build with this type of with a small model if you're