I wonder why companies like IBM are jumping on the LLM bandwagon and training/releasing models that have no chance of competing with Llama/Mistral? To me it just looks like a complete waste of $$ because nobody will use them in any serious scenarios
Their customers aren’t going to build their own RAG and agent frameworks, vector DBs, data ingest pipelines, finetunes, high scale inference serving solutions, etc, etc.
There’s an incredible amount of stuff to buy.
That data issue is important enough for some companies to pick mediocre model over llama or mistral.
IBM and other big players are vigilant about these things, and this is what companies pay for.
Their software may not be better in some metrics, but they're cleaner in some and their support contracts allows people to sleep tight at night.
This is what money buys. Peace of mind and continuity.
And more importantly, IBM will guarantee it in the case that they're wrong. _That's_ what companies pay for.
So will OpenAI, according to Sam Altman. Can they be trusted?
IBM has proven itself in various ways over the years, OpenAI hasn't.
While IBM is a behemoth of a money making machine, they put money where their mouth is. OpenAI does not.
So I'll trust IBM, but not OpenAI.
This support contract stuff: what are you talking about? You download these models, you use them. What would you pay for? It’s not clean data, they say it’s clean: why would I pay liars? Let’s game out the indemnity idea. I pay $10k/mo for 12 months. Then OpenAI loses v. NYTimes, ruled LLM training is not fair use, need express permission. IBM pulls the models. What the hell did I pay $120k for? And by the way, you can pay a law student 1 beer to tell you OpenAI is going to lose because of Warhol v Goldsmith. You can do whatever you want with your money, but I personally would not waste it on worthless indemnity.
I know the Stack is not clean, because they included my fork of GDM's greeter, which is GPL licensed.
My words about IBM was in general. I can't tell anything about their models, because I didn't see mention of "The Stack", and I don't know what their models are based on.
On the other hand, IBM doesn't like risks from my experience, so they would play it way safer than other companies.
If their data is not clean to begin with, then shame on them, and hope their AI efforts burn to the ground.
BTW, LLM training is not fair use. For start, Fair Use's definition automatically excludes "for profit" usage. Just because OpenAI has a non-profit part and training done here doesn't make them immune to consequences of for profit operations.
There will be market for their services. Maybe a different one, but there will be.
The gist is still current, but you need to fill in AWS as the current uncontroversial choice.
But I’m pretty sure both models have “we’re not responsible” clauses.
Citation needed
All I've seen from them in my professional experience is actually legacy mainframe maintenance.. Not shovelware, but very far from hardcore tech.
PALO ALTO, Calif. – IBM defined at (trade show ed.) Hot Chips a new interface for the 2020 version of its Power 9 CPUs. The Open Memory Interface (OMI) will enable packing on a server more main memory at higher bandwidth than DDR, and as a potential Jedec standard could rival GenZ and Intel’s CLX.
OMI basically removes the memory controller from the host, relying instead on a controller on a relatively small DIMM card. Microchip’s Microsemi division already has a DDR controller running on cards in IBM’s labs. The approach promises to deliver up to 4TBytes memory on a server at about 320GBytes/second or 512GB at up to 650GB/s sustained rates.
https://research.ibm.com/blog/albany-semiconductor-research-... etc
IBM doesn't have fabs, but they still do R&D into semiconductors that very much target future commercial processes. They do a fair bit on quantum computing too, to name just a couple of things.
If IBM split off half of their mainframe division and let some competition get going I think the segment could actually be something to contend with.
The basic idea of the IBM mainframe is almost perfect for what a lot of companies actually need (massively reliable hardware to support lots of middling software; most work is shunting data around) but everyone knows they're going to get locked into IBM.
There really aren't a lot of companies out there that can claim to do similar (and of course besides s390x, an ancient and venerable CISC, IBM also has Power, so they are doing this 2x over). You'll find a lot of IBM employees contributing to what I'd consider "hardcore" tech like LLVM and the Linux kernel as a result, because they genuinely have a large amount of expertise in those and similar areas. And here I'm not even really including Red Hat, but if you include them then they are even more overweight in the hardcore tech category.
If anything, a lot of the rest of the tech industry has left "hardcore tech" behind due to efficiency concerns as a result of a longrunning industry wide process of consolidation and commodification that IBM has resisted for obvious reasons. IBM is hardcore to a fault if anything.
TLDR: I actually think IBM punches above their weight in the "hardcore tech" area so long as our definition is sufficiently low level rather than say, cloud services, in which case fair enough you can probably fairly say they suck at that.
Here I've also chosen to entirely ignore IBM research.
They've been doing "AI" for ages. Notably Watson over the last couple of decades or so.
I've not seen any proper evaluations for Granite against, say, Llama or Mistral.
Until we do it's probably too early to say they can't compete, at least in some areas where others perform poorly.
Previous Granite models were on the level of first llama in my benchmarks.
I’m expecting this version to be roughly comparable to llama 2
Did you even read the benchmarks they post on that link? Assuming they're not outright lying, their 8B model is superior to Llama/Mistral models of the same size for coding tasks.