Avalon local LLM

[Pocster]

8 hours ago, -rick- said:

@Pocster How desperate are you?

 

 

Well ! Unfortunately as I presume you now realise the ram shortage is a massive problem - yes , even for Apple .

It did look obvious tbh - so now I have to wait for any indicator of ultra appearing and jump on it ! I guess it could be announced at wwdc - maybe have a date for pre orders . Buys Apple time to acquire ram whilst still be bigging up their product . That’s the best I can hope for - a definite date to refresh Apple Store !

[-rick-]

https://arstechnica.com/gadgets/2026/05/apple-may-take-several-months-to-catch-up-to-mac-mini-and-studio-demand/

 

Interestingly it mentions TSMC being a bottleneck. TSMC doesn't make RAM.

Edited 18 hours ago by -rick-

[Pocster]

2 hours ago, -rick- said:

https://arstechnica.com/gadgets/2026/05/apple-may-take-several-months-to-catch-up-to-mac-mini-and-studio-demand/

 

Interestingly it mentions TSMC being a bottleneck. TSMC doesn't make RAM.

TSMC doesn’t make RAM, no one said they do. TSMC is the SoC bottleneck.

 

The RAM shortage is a separate bottleneck, and Apple clearly isn’t immune. Other manufacturers have openly said memory shortages are affecting them, and Cook has said Mac mini / Mac Studio supply-demand balance will take several months.

 

High-memory Mac mini / Studio configs are also exactly where the worst delays and unavailable options have shown up.

 

There are also multiple reports suggesting the memory shortage could last well into 2027 and possibly beyond, especially as AI demand keeps soaking up supply.

 

So pointing out that TSMC doesn’t make RAM doesn’t disprove the RAM issue; it just identifies a different bottleneck.

[-rick-]

9 minutes ago, Pocster said:

So pointing out that TSMC doesn’t make RAM doesn’t disprove the RAM issue; it just identifies a different bottleneck.

 

Sure, I just thought it was interesting to highlight.

[Pocster]

Even if Mac unified memory and iPhone RAM aren’t identical final packages, they come from the same constrained LPDDR supply chain. Apple then has to allocate that supply across products, and iPhone will obviously take priority over niche high-memory Mac configs.

[MikeSharp01]

3 hours ago, Pocster said:

Even if Mac unified memory and iPhone RAM aren’t identical final packages, they come from the same constrained LPDDR supply chain. Apple then has to allocate that supply across products, and iPhone will obviously take priority over niche high-memory Mac configs.

I suppose that consumer high end local LLM schemes need large memory but you can do the same thing with a cloud based one while you await delivery of enough RAM, or whatever other semiconductor you are waiting on. Trouble is that the end of the proverbial rainbow is moving away from you faster than you can stuff memory into machines anyway, so, when your RAM arrives there will be plenty of models it won't run because they have grown, bloated, faster than you can add RAM to your machine. 

[-rick-]

12 minutes ago, MikeSharp01 said:

I suppose that consumer high end local LLM schemes need large memory but you can do the same thing with a cloud based one while you await delivery of enough RAM, or whatever other semiconductor you are waiting on. Trouble is that the end of the proverbial rainbow is moving away from you faster than you can stuff memory into machines anyway, so, when your RAM arrives there will be plenty of models it won't run because they have grown, bloated, faster than you can add RAM to your machine. 

 

Does look that way. New Deepseek one for example, and it's been touted has having huge optimizations for reducing memory usage. It might be much better at storing context but if the model is so huge in the first place it's not easy to run at home.

 

Google seems to have done well with Gemma, but of course that is a lot cut down from the leading models and they aren't releasing those. Doubt any western firm will for 'safety' reasons. So far the Chinese govt doesn't seem to care about that so long as their models spout Chinese propoganda at the relevant moments.

[Pocster]

8 hours ago, MikeSharp01 said:

I suppose that consumer high end local LLM schemes need large memory but you can do the same thing with a cloud based one while you await delivery of enough RAM, or whatever other semiconductor you are waiting on. Trouble is that the end of the proverbial rainbow is moving away from you faster than you can stuff memory into machines anyway, so, when your RAM arrives there will be plenty of models it won't run because they have grown, bloated, faster than you can add RAM to your machine. 

I’ll use chat as my reasoner but I need local to  do my local actioned stuffed until I can source enough ram . Bigger model did mean better but now smaller better optimised models can out perform a larger one . Obviously there’s a point when you can’t go smaller without too much compromise . If you ask ChatGPT itself how long before a model matches its capabilities of today in a ‘ reasonable ‘ size it reckons within 2 years a 256gb model will be comparable to ChatGPT 5.4. That’s insane ! To have that capability sat on your desk in a relatively low ram config.

Moe and potentially’ bolt on ‘ specialists seems to be the next level . A medical specialist , maths specialist etc plugged in to the reasoner.