It's absolutely true. I have three different setups here, a rig with 8 gpus that I use in either one of two modes (all 8, very heavy model or just one much lighter) and two DGX sparks. I haven't used a paid model in months. Sure, they use a few more tokens and the results may be slightly less good (but this doesn't really matter because I'm still in the loop so it is easy to correct). But the fact that I'm not sharing my work with the paid providers is much more to my liking than having big brother looking over my shoulder at every step and occasionally giving me the 'I can't let you do that, Jacques' treatment.
Working on a blog post to document it all, but that will take a few more days. The short version: the big rig is probably $25K, ttft is dependent on how much prefill you have but typically a couple of seconds (2K prefill/sec in one setup, about 500 in the other), tgs 100 to 150 or so depending on what it is doing and how well the speculative decoder works with the current context. Electricity is pretty bad, full tilt it's about 2500W or so but I've power limited it to 1500 to keep it civil. It is not cheaper than using a frontier lab (or at least, I don't think it is), but the independence bit is the important part for me and I don't care if that carries a surcharge, even if it is a substantial one.
I mean, I just have 4xV100S GPU and a Macbook Pro w/48GB of RAM and I find it kind of amazing.
My server can run Qwen Flash Next at 500 t/s aggregate (about 200 t/s single user). It's pretty great. I use it for local coding and all my worker agents. It is about as reliable as Sonnet 5 for agentic coding. I make 3D models, keep the home network and server updated, fully built ansible files.
You do NOT need 20K in hardware to run these models reliably. I built my own inference engine from scratch for fun. You can run Strata on 16-32GB cards at very respectable token rates if you have the standard RAM to go with it. I strongly encourage people to go use this model on OpenRouter and try it out for tasks.
I also have a somewhat lobotimized quant of GLM Flash 5.3 working and it's pretty decent too.
> Sure, they use a few more tokens and the results may be slightly less good (but this doesn't really matter because I'm still in the loop so it is easy to correct)
I can't wait to use models that are 'slightly less good' when I am at work instead of something like Fable that lets me focus on the problem at hand as much as possible rather than also spending additional effort correcting for the tool I am using...
You need to reread what was written. No one is proposing to take away your right to leak proprietary edge to the rapacious frontier labs that need to eat most of the economy in order to survive.
"Affordable" and "IBM" don't belong in the same sentence together, but they're happy to sell you their inference-capable hardware if you've got the wallet for it.
I don't agree with that. They made the PC revolution possible by making hardware that was at least affordable enough to be bought by a large number of businesses and individuals. It also set the stage for the clone market. I'm pretty sure that if IBM really set their mind to it that they could do this.
That doesn't detract from your overall point that they tend to make overpriced stuff, but I don't think they have to do that. Besides, what's overpriced? NVidia's GB300 workstation sells for 95K euros, a single RTX 6000 is now 20K. Surely even IBM can compete at that level.
True, even so: They still make some pretty impressive hardware. They have access to fab capacity, they know how to make chips. They have the capital. If they really wanted they could do it.
In a way it is, but I would see that as a starting point, not as the ready solution. They'd need to do what NV has done for their eco-system: fantastic libraries, a credible future upgrade path, easy ordering, manufacturing partnerships, the works. I figure besides the performance gap that they are at least 5 to 10 years behind NV with their present offering. But NVidia is dropping stitches and IBM should take advantage of that.
When that happened I'd been working at Tandy (EU version of Radio Shack) for a while and even though we sold computers they were not taken serious until IBM came along and then suddenly we couldn't supply them fast enough.
Yes, that was the first time I had a 'proper' processor to play with. A Dragon 32 (CoCo clone from the UK).
I've never seen Tandy used outside of Europe but then again, I had not traveled as much then as now so it's definitely possible they used the brand on other continents as well. I wonder how many trees were cut for those catalogs :)
I know. But: they have gobs of money, they still have some talent, they still have a hardware department that I'm pretty sure would jump at a chance to give NV a run for their money. This would require a massive long term investment and a commitment that I'm not sure they would have the stomach for.
They do? They are a spec in the ocean relative to big players offering inference and training their own models. Pretty sure one thing they are struggling with is the money to compete successfully in the ai game this time around.
Yes but unironically? IBM should be what NVIDIA would be competing with their NVL72 and DGX products. It doesn't make sense that IBM is just watching NVIDIA take its lunch.
Considering the disclosed numbers for DeepInfer and the usefulness/popularity of DeepSeek 4.1 Flash, I expect it is inevitable that more companies are going to consider the self-hosting option seriously, and perhaps play with the idea of offering inference as a vendor.
While he thinks most businesses can build useful AI infrastructure in just two or three racks, he feels many will struggle to get it running.
“The question becomes: how do you do that?” he said. “The question that the enterprise struggles with is: ‘Where are my kernel engineers? Where are the people who worry about the underlying network? Who can make sure that the model is clustered on GPUs that are close to each other in network proximity? Who can make sure that you’re doing the right caching of results so you get an incredible response?’”
Has he heard of “agentic AI”? You simply fire up Claude or ChatGPT and ask it how to do all of the above.
That's based on the idea that humans can learn and adapt and solve problems, and that's not what companies want to think about when they're seeking a profitable transaction such as hiring.
Do not do that if you can't prompt it correctly. It is a potential disaster waiting to happen. LLMs are likely to pull things off the shelf without real defense in depth and that's a problem. Maybe slightly less if it's only in an internal network, but still.
Can we please stop giving weight to CEOs talking their book? Like of course IBM wants you to think this, it would be great for them if it’s true. And maybe it is true! But IBM’s CEO saying it’s true tells me just as much as if a frontier lab said it’s not: nothing.
What happens when your competitors invest in better and more expensive tools or materials in general? They (potentially) make better products, but those products also cost more. It's turtles all the way down.
OpenAI most likely pushed Cerebras into a contractual agreement not to do business with Anthropic through 2030. ("Most likely" because the names of the companies Cerebras is contractually obligated not to sell to are redacted in public filings)
I’ve been in tech for a long time… I have no idea what IBM does. Now, I get that I’m not the customer, that their customer is “business” but not my business… no… bigger business. The biggest of businesses I assume. I think it must either be a club I’m not in, or IBM is three half racks in a trenchcoat submitting invoices for millions to companies where people just pay it because “it’s IBM, it must be important”.
Are you comparing to subscription prices or API prices? Because if it's the former it won't last forever.
My server can run Qwen Flash Next at 500 t/s aggregate (about 200 t/s single user). It's pretty great. I use it for local coding and all my worker agents. It is about as reliable as Sonnet 5 for agentic coding. I make 3D models, keep the home network and server updated, fully built ansible files.
You do NOT need 20K in hardware to run these models reliably. I built my own inference engine from scratch for fun. You can run Strata on 16-32GB cards at very respectable token rates if you have the standard RAM to go with it. I strongly encourage people to go use this model on OpenRouter and try it out for tasks.
I also have a somewhat lobotimized quant of GLM Flash 5.3 working and it's pretty decent too.
I can't wait to use models that are 'slightly less good' when I am at work instead of something like Fable that lets me focus on the problem at hand as much as possible rather than also spending additional effort correcting for the tool I am using...
That doesn't detract from your overall point that they tend to make overpriced stuff, but I don't think they have to do that. Besides, what's overpriced? NVidia's GB300 workstation sells for 95K euros, a single RTX 6000 is now 20K. Surely even IBM can compete at that level.
Yeah they were only half a decade late to the PC revolution back in the day...
I've never seen Tandy used outside of Europe but then again, I had not traveled as much then as now so it's definitely possible they used the brand on other continents as well. I wonder how many trees were cut for those catalogs :)
They do? They are a spec in the ocean relative to big players offering inference and training their own models. Pretty sure one thing they are struggling with is the money to compete successfully in the ai game this time around.
If you have ever worked at IBM, it makes total sense. Completely dysfunctional middle management and on up.
https://karlbode.com/ceo-said-a-thing-journalism/
we technologists tend to extend what we see and think to other realms where it doesn't make sense
Cerebras with Claude 8?