Everyone thought CES 2026 would be about

faster GPUs. That's not what Nvidia did.

Instead, they quietly dropped something

way bigger. The Reuben platform isn't

just a new chip. It's six chips fused

into an AI supercomputer. And Nvidia

says it can cut inference costs by up to

10x. That sounds amazing, but here's the

real question. Is Reuben actually a

breakthrough, or is it just really

expensive project that locks you deeper

into their ecosystem? We have videos

coming out all the time. Be sure to

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All right, so you might think Reuben is

just Blackwell but faster. It's not.

Reuben, also called Vera Rubin, is

Nvidia's first move into annual platform

releases. And instead of shipping a

single monster GPU, Nvidia changes

everything. Reuben is a six chip

platform which is designed to act like

one machine. Here's what that actually

means in real terms. Instead of GPUs,

CPUs, memory, and networking fighting

each other, they're tightly coupled from

the get-go. GPUs, Vera CPUs, NextG Envy

Link, and ultraast memory, all

engineered together. The big unlock here

is NVLink 6, where each GPU can push a

whopping 3.6 tab per second. Scale that

up to a full NVL72 rack, and you get 260

tab per second of internal bandwidth.

That's 72 GPUs and 36 CPUs behaving like

a single system. Then there's the HBM4

memory. In top configurations, memory

bandwidth hits over 1500 tab per second.

Okay, that was a lot of jargon. So, what

does this really mean for us? It isn't

about training a little faster. This is

rack scale AI built for longchain

reasoning, simulation, or massive agent

coordination. Reuben systems ship later

in 2026 and then Reuben Ultra is

supposed to follow that in the second

half of 2027. Here's where this gets

personal for developers. Reuben isn't

just about training. It's about

inference. Nvidia says Reuben can run

the same workloads with up to 4x fewer

GPUs than Blackwell. And in some cases,

token costs drop to one/10enth. That

matters if you're running multi- aent

systems, long context reasoning models,

or real time physical AI. Fewer GPUs

sound simpler, but the thing is when

throughput explodes like this,

observability becomes the bottleneck.

Trillion parameter models generate

massive volumes of logs, metrics, and

traces. When an agent fails, you don't

have time to dig through the terabytes

of noise. You need answers immediately.

That's where platforms like Better Stack

become critical. monitoring inference

latency, error rates, and system health

across these giant interconnected racks.

Now, there are some trade-offs cuz

you're still locked into Nvidia's

ecosystem, and the power requirements

here are pretty serious. But the upside,

you're getting more bandwidth inside one

rack instead of the entire internet.

That unlocks things we simply couldn't

do before. Here's the good news. You

don't need Reuben hardware today to

prepare. If Reuben hits the way Nvidia

claims, the winners will still be teams

that adopt this early. That means

optimizing inference efficiency, using

quatinization and smarter batching,

building real observability into your

pipelines. Now, will you target Ruben in

2026? And how will Ruben change what

you're actually building? That could be

the question here. We'll see you in

another