Earlier quoted context omitted.
you keep the model. it's never deprecated. with closed ai, you are forced into a new more expensive model every few months. if an open model infra provider does that, you simply switch to another one. it's not in their interest to do that.
That's not really true though, providers are deprecating models and I have at least 10 emails to prove it.
Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
21–30 of 53 posts
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#22Earlier quoted context omitted.
Their pricing is incredible on the token plan - something like 50b tokens for $60!
No that's not right. It's 50b credits, not tokens. What is a credit? Nobody knows. Someone did the math a few months ago and paying API prices was the same as the monthly subscription.
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#23Earlier quoted context omitted.
That's not really true though, providers are deprecating models and I have at least 10 emails to prove it.
Nothing stops you from downloading the model and hosting it on a cloud virtual machine
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#24Earlier quoted context omitted.
Their game? Sell me tokens instead of me buying them from an American lab for a higher price. Publishing open weights gives me more confidence in the model, and ironically makes me less anxious about making sure I can replace the cloud usage with a local alternative. Whereas I’m very nervous right now with relying on 5.6-Sol - what if they triple the price, nerf it, etc.?
> Publishing open weights gives me more confidence in the model Why? It's not like you can audit weights like you can with code. > what if they triple the price, nerf it, etc.? What if an open weights infra provider does that? What's the difference?
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#25Earlier quoted context omitted.
you keep the model. it's never deprecated. with closed ai, you are forced into a new more expensive model every few months. if an open model infra provider does that, you simply switch to another one. it's not in their interest to do that.
That's not really true though, providers are deprecating models and I have at least 10 emails to prove it.
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#26Such a well written article, refreshing to read in between all the slop. I've used Mimo extensively in the past few months, can't wait to see what v3 will bring.
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#27Earlier quoted context omitted.
> Publishing open weights gives me more confidence in the model Why? It's not like you can audit weights like you can with code. > what if they triple the price, nerf it, etc.? What if an open weights infra provider does that? What's the difference?
Because I can run Qwen 3.6 or DeepSeek V4 until the end of time if I want to? The model is on HuggingFace; anyone can download it. I have Qwen and Gemma on my laptop right now if I want to use them, even if I decide to go be a hermit who never interacts with the outside world again.
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#28Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#29Earlier quoted context omitted.
No that's not right. It's 50b credits, not tokens. What is a credit? Nobody knows. Someone did the math a few months ago and paying API prices was the same as the monthly subscription.
Yep, roughly the same. If you max out the sub each month it’s roughly 20% cheaper if you also carefully use all their promotions. I’ve thrown $50 at it, use UltraSpeed liberally and have yet to exhaust it.
I added 20 USD in credits for the Xiaomi models a while ago and they've been happily writing and updating hundreds if not thousand of pages and I still have 7 USD left!
Re: Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit
#30I've been saying efficiency is the next "frontier" in AI, at least for LLMs that people use daily. Companies have started to really balk at token costs from the major providers, and there's some evidence that the cheaper Chinese models are chipping away at Anthropic/OpenAI dominance from below (as cheaper Chinese products have done in other industries for many years). I continue to think that the vendor that figures…
Karpathy calls it a "Cognitive Core", and it's essentially a small model that learns to reason and look up the data it needs as opposed to a giant model that memorizes all the data in the world and tries to process large chunks of it all at once with every thought. I think it will be based on the thing that grokking, the lottery ticket hypothesis, and the universal weight subpspace hypothesis all point to.
Eventually someone will figure out how to build it and the entire economy that we've now built on top of the wacky idea that nothing can possibly ever get more efficient will collapse overnight.
Sometimes I wonder how much Nvidia would pay someone not to release a thing like that, and then I wonder if that's already happened.