This is the kind of update I like to see. Too many sports watches launch with good hardware but take months to add useful training features. The Hyrox tools and Virtual Pace mode should be especially useful for people training for specific events rather than just tracking runs.

I'm also interested in the new energy tracking system. If Amazfit can make it reasonably accurate, it could be a nice alternative to Garmin's Body Battery feature. It's good to see Amazfit continue adding features to existing devices rather than forcing users to buy new heardle hardware every year.

PS: The Spark/GB10 chip in a Yoga Pro 9n and not in a Legion doesn't exactly point to the rumors, that the Spark's performance is going to be between a 5070 Laptop and 5070 Ti Laptop. Also, techpowerup.com/gpu-specs/nvidia-gb20b.g1114 states that the chip is built on a 5nm node, not 3nm. Another not exactly reassuring performance rumor expectation, more like a rumor buster. But we will see.

And why do both of the following state that DirectX is not supported?

Quote from: techpowerup.com/gpu-specs/gb10.c4342Built on the 5 nm process, and based on the GB20B graphics processor, the card does not support DirectX. Since GB10 does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games.

Quote from: techpowerup.com/gpu-specs/nvidia-gb20b.g1114NVIDIA's GB20B GPU uses the Blackwell 2.0 architecture and is made using a 5 nm production process at TSMC. GB20B does not support DirectX.

Grösse des iPhone Air 2 auf die des iPhone 13 Mini reduzieren.

2499 CHF kassieren.

Wird sofort gekauft.

QuoteThe company claims it can run 120 billion parameter models locally

Thing is:

Quote from: reddit.com/r/LocalLLaMA/comments/1u8kr2o/we_need_a_80160b_model_urgently_the_unified/We need a 80-160B model urgently. The unified memory device market needs more Models.

(read full text there)

For anyone who doesn't know, an alternative from AMD is called Strix Halo:
AMD Strix Halo: 256 GB/s = 256-bit * 8533 MT/s / 1000 / 8.
NVIDIA Spark: 273 GB/s = 256-bit * 8533 MT/s / 1000 / 8.

There are so many recently released 200B to 300B parameters MoE models, that it would be really nice, if these devices had twice the memory (= 256 GB RAM / unified memory). Then a good quant of those models (see below) could be run.

An alternative in the meantime could be to run a Qwen3.6-27B quant on a used 24 GB VRAM GPU (typically RTX 3090) (24 GB is the absolute bare minimum), a model that performs much better for its parameters size, because it's a dense model, see:
artificialanalysis.ai/?models=gpt-oss-120b%2Cgpt-oss-120b-low%2Cgemma-4-31b%2Cgemma-4-31b-non-reasoning%2Cmistral-medium-3-5%2Cdeepseek-v4-flash-non-reasoning%2Cdeepseek-v4-flash-high%2Cdeepseek-v4-flash%2Cminimax-m2-7%2Cstep-3-7-flash%2Cmimo-v2-5-0424%2Cqwen3-6-35b-a3b%2Cqwen3-5-122b-a10b%2Cqwen3-6-27b%2Cqwen3-6-27b-non-reasoning%2Cqwen3-6-35b-a3b-non-reasoning%2Cqwen3-5-122b-a10b-non-reasoning&intelligence=artificial-analysis-intelligence-index

84,4% Marktanteile durch 2,007 Milliarden zumindest gelegentliche Nutzer der drei Großen bedeutet 2,38 Milliarden solche LLM-Nutzer weltweit. Bei über 8,3 Milliarden sind dies knapp 29% der Weltbevölkerung. Bei 71% +-3% oder 5,9 Milliarden Internetnutzer der Weltbevölkerung sind es etwa 40% aller Internetnutzer weltweit. Das ist nach nur ein paar Jahren seit Entstehung der großen LLMs ein außerordentlich beachtlicher Anteil. Ich nutze allerdings keine LLMs, auch da ich lieber eine spezialisierte KI mit außerordentlich besserer Qualität nutze.