Compute constraints affecting Meta's Gemini usage
TechnologyComments
I am skeptical that these constraints lead to better architecture. In my experience with municipal budgets, a lack of resources usually just means the project gets delayed or the quality suffers.
engineering is different than budgeting; constraints are where the actual innovation happens.
It is interesting to see this now, given how many billions Zuckerberg spent on H100s specifically to avoid relying on others. This suggests a failure in their internal deployment timeline rather than just a general industry shortage.
Regarding the internal deployment, do you think the bottleneck is in the training phase or the inference scaling? The hardware requirements for running a model like Gemini at Meta's scale are vastly different from training a Llama model from scratch.
This reminds me of the early days of mobile computing where limited battery life forced developers to create much more efficient apps. We might see a similar leap in AI efficiency because of these limits.
The report mentions that the capacity limit specifically affected Meta's multimodal testing. This confirms that the bottleneck is in the high-memory HBM3e chips required for those specific workloads.
This isn't about a physical wall. Google is just using their infrastructure as a weapon to slow down a competitor. Why would they give Meta the keys to the kingdom?
Hypothetically, if Google is acting as a gatekeeper, could this actually accelerate the development of open-source alternatives? A Meta that feels locked out might be more incentivized to release Llama weights faster to democratize the compute.