The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
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Liang: SambaNova 10kW SN40 rack outperformed 140kW Nvidia GPU racks
“And so by the time we released SN-Forty a couple years ago, it became incredibly popular, because instead of a 130, a 140 kilowatt rack of NVIDIA GPU, we were outperforming them with a 10 kilowatt SN-Forty rack.”
Liang: SambaNova serves 1.5T parameter models on one rack versus 10-20
“And so with sum it over, that minimum quantum is down to one rack. Right, where if you have other, other service providers, you just run, say, a DeepSeq model, which is now one and a half trillion parameters, just to run that, the minimum for some of the other…”
Liang: Agentic AI will drive a wave of mid-sized distributed data centers
“And I think you're going to see this new wave of companies that are doing distributed data centers, right? So these data centers are mid-sized, right? They're mid-sized, and it's going to be even more important as you go into this agentic world”
Liang: Frontier AI models are heading toward 10 trillion parameters
“The new models are heading towards 10 trillion. Even the open source models are already one to two trillion parameter models, and so now you're starting to see these models getting very big because people are looking for accuracy, right?”
Liang predicts data centers will support only three to four AI chips
“As much as these data centers and service providers are heterogeneous, right, they're using different chips, NVIDIA and other chips, it's not going to be a hundred different chips. It might be two or three. Maybe three or four, right? That's as heterogeneous a…”
Liang: Relying on commodity AI models will compress enterprise margins within two years
“If you actually transfer all of those You know, services, that differentiation, to all using the same exact model. That's in the community. Where does the differentiation come from? Right? And so what most companies start to realize, if you just fast forward, …”
Liang: AI inference chip deployments will dwarf training by orders of magnitude
“Because at scale, the number of chips deployed for inferencing will be orders of magnitude greater than whatever you're doing for training.”
Liang: Demand will converge on the fastest, most accurate large models
“And so, as the cost of delivering fast goes down, you're going to see most people switch over to the fastest. And this is why I feel like, you know, the premium inference, which is large models, which equals the most accurate. The most accurate models and fast…”
Liang: Infrastructure repatriation to on-premises is actively occurring across enterprises
“Look, repatriation of infrastructure into on-prem is definitely happening, right? You saw this big shift. Everybody's got a cloud called cloud, you know, everything's in the cloud, and 20 years later, you still have companies just starting the migration to the…”
Liang: AI energy, data center, and chip constraints will only worsen
“The hints that you're seeing today with energy constraints, data center constraint, chip availability constraints, cost constraints, all of those things are only getting exacerbated, right?”
Liang: SambaNova inference uses standard air-cooled racks and Ethernet
“Standard 19 inch rack, standard air cooling, no complicated liquid cooling retrofit in the data center. We're using standard Kubernetes, standard Red Hat Linux, standard Ethernet at the top for networking. We don't have to use all this kind of really expensive…”
Liang: Japan and South Korea are investing heavily in sovereign AI models
“Countries have started doing this work and you see this in Japan and Korea announced the same thing you see in other parts of the world where they're investing a significant, significant amount of money to actually train from scratch. Train their own national …”