61–70 of 345 posts
I'm not seeing any update to the app on my android phone... maybe later today?
>We’ve published an in-depth technical explainer
I was expected a pdf link, but this goes to a brief article on twitter/X. lol, okay...
I find it puzzling Google doesn’t actively promote its own cloud for inference of Gemma 4. Open source is great, love it. But shouldn’t Google want me to be able to use and pay for it through Gemini and vertex?
A key thing to understand about Google is that under the hood is a collection of extremely powerful fiefdoms (many of which would stand as their own fortune 500, hell 100) that are all trying to act in their own interest. It's almost closer to a conglomerate than a company, where Google needs to bid internally against external players for resources. If Gemma 4 is less lucrative than Claude to the Google Cloud kingdom…
MTP support is being addedto llama.cpp, at least for the Qwen models ( https://github.com/ggml-org/llama.cpp/pull/20533 ) and I'd imagine Gemma 4 will come soon. The performance uplift on local/self-hosted models in both quality and speed has been amazing in the last few months.
I have a dumb performance question. Why when asking a model to change text in a minor way; are we not asking it to generate the operational transformations necessary to modify the text, and then just executing the ot on the existing text vs reproducing every token? Maybe tools are doing that more than I realize?
Beta but useable
if someone wants to work with gemma and dont deal with ollama or configs - there is (my baby) https://airplane-ai.franzai.com/ Beta but useable
Focusing more on performance to compute efficiency over pure performance. And maybe that’s why Gemini is (seemingly) lagging behind?
Other providers hitting capacity and hitting the limits subsidising their inference.
Google strategy seems to be about scaling and distributing these models to their existing billions of users.
Earlier quoted context omitted.
There was a startup posted here which built custom hardware that let the AI respond instantly. Thousands of tokens per second.
Groq.
I don't see it talked about much, but Gemma (and gemini) use enormously less tokens per output than other models, while still staying within arms reach of top benchmark performance. It's not uncommon to see a gemma vs qwen comparison, where qwen does a bit better, but spent 22 minutes on the task, while gemma aligned the buttons wrong, but only spent 4 minutes on the same prompt. So taken at face value, gemma is now…
Anecdotally the 15/month basic Gemini plan allows coding all day. I'm not hitting the limits or needing to upgrade to 100/month plans like other people are doing with Claude or Codex. Caveat: Gemini has been dumbed down a few times over the last year. Rate limits tightened up too. So it might not be this good in the future.
Antigravity seems significantly better in comparison, but with lower usage limits. If I run out, I usually don't bother switching to Gemini CLI.
MTP support is being addedto llama.cpp, at least for the Qwen models ( https://github.com/ggml-org/llama.cpp/pull/20533 ) and I'd imagine Gemma 4 will come soon. The performance uplift on local/self-hosted models in both quality and speed has been amazing in the last few months.
I have a dumb performance question. Why when asking a model to change text in a minor way; are we not asking it to generate the operational transformations necessary to modify the text, and then just executing the ot on the existing text vs reproducing every token? Maybe tools are doing that more than I realize?
I don't see it talked about much, but Gemma (and gemini) use enormously less tokens per output than other models, while still staying within arms reach of top benchmark performance. It's not uncommon to see a gemma vs qwen comparison, where qwen does a bit better, but spent 22 minutes on the task, while gemma aligned the buttons wrong, but only spent 4 minutes on the same prompt. So taken at face value, gemma is now…
True, but you have to add up the cumulative token output if your being fair. That alignment issue requires another set of input and output tokens to correct.