With other software, devs convince their managers of the importance of using open source stuff in their stack. With AI, it's usually managers choosing what models to use for the devs. The US labs don't need to give a damn how much devs like open source
I was dev, and now I am Senior level manager.
Open Weight models are current main focus for many companies with full alignment with top management for very simple reasons:
- stable and predictable performance (no pre-launch models degradation)
- ability to tune them for specific business cases (though still rare tbh)
- better (at least 60% Opus vs Kimi (real,3rd party)) and more competitive pricing
- flat pricing if tokenusage is big enough to justify renting GPU
- decent quality
- much higher guarantees that data will not be sent somewhere (assuming 3rd party inference providers)
- and cherry on top: flat and minimal pricing with absolute confidentiality using Alibaba Apsara stack of recently released AMD Instinct Coder box[1]
impulser_ · · focus · HN ↗
Where is the cool shit from the US labs?
culi · · focus · HN ↗
reddec · · focus · HN ↗
[1] <a href="https://www.amd.com/en/ecosystem/oem/supermicro/amd-instinct-coder.html" rel="nofollow">https://www.amd.com/en/ecosystem/oem/supermicro/amd-instinct...