🤥 GATE Keeper NVIDIA doesn't Help... where's the RAM

Products & Technology
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:lying_face:
NVIDIA raising the Jetson Orin Nano Super from $249 to $399 is a 60% cash grab, plain and simple.

Let’s be real: dev kits used to be loss leaders designed to bring developers, tinkerers, and researchers into an ecosystem. You build cool stuff, deploy on their hardware, and scale up.

Slapping a 60% price hike on existing silicon in the middle of its lifecycle isn’t “supply chain adjustments”—it’s taking advantage of developer lock-in. Silicon fabrication costs on mature nodes don’t spike by 60% overnight. It’s just exploiting the artificial hype and forcing the maker and robotics community to pay enterprise margins.

When you price entry-level hardware out of reach for students, makers, and independent labs, you aren’t inspiring the next wave of AI innovation—you’re gatekeeping it.

Meanwhile, open-source alternatives with real industrial I/O, dedicated hardware video encoders, dual 2.5GbE, and efficient NPUs are sitting around the sub-$280 mark. Developers are going to vote with their wallets and start porting their vision pipelines to open silicon.

Shame on NVIDIA. The green tax is officially out of hand.

The push to funnel everyone into cloud-hosted AI was a calculated land grab to monetize compute as a recurring subscription rather than a one-time hardware sale. Big Tech convinced developers that edge hardware couldn’t keep pace with massive foundation models, pushing workloads into centralized data centers where they could rent you back API calls at high margins. Now that latency, bandwidth constraints, token billing, and severe privacy concerns have exposed the flaws of pure-cloud architectures, the industry is scrambling to “bring AI back to the edge.” But instead of building purpose-driven edge silicon from the ground up, legacy giants are trying to shoehorn bloated, power-hungry datacenter architectures into overpriced dev kits while charging enterprise premiums for basic entry points.

Gatekeeping the entry barrier with steep hardware price hikes is the exact opposite of fostering an innovative ecosystem. Historically, true technological revolutions happen when hardware is cheap, accessible, and democratized. Letting students, independent makers, and grassroots developers experiment without burning thousands of dollars. By pricing entry-level kits out of reach, NVIDIA is prioritizing quarterly wall-street margins and ecosystem lock-in over open community growth. Open silicon platforms, accessible NPUs, and local-first open models are already proving that practical edge intelligence doesn’t need to be locked behind proprietary tollbooths, and developers will inevitably migrate to hardware that respects both their budgets and open architecture.

Add a B.S. made up "United States of America " protectionist government overreach and corruption, are we cooked?
:face_with_peeking_eye:

As You were.. :v:

GL :slight_smile: PJ :+1:

Yeah, this one’s a doozy. From The Verge/Bloomberg (Aug 22, 2026):

:fire: “Nvidia’s AI chips are about to get more expensive too”

• >15% price hike on server GPUs — Nvidia already notified its biggest customers (Oracle, Microsoft, and the data center builders behind them)
• Comes on top of earlier GeForce price hikes this year (RTX 5080 already got bumped at Best Buy)
• Blame game: Nvidia blames the AI demand spike driving up component prices — the same AI boom their own chips created
• Q2 earnings report dropped this past Wednesday (Aug 27), so we got the full picture now

The really spicy part: customers building AI infrastructure for the people who also bid up GPU prices are getting squeezed on both ends. Classic monopoly rent-extraction once you’re the only game in town for CUDA-accelerated compute.

Probably not great for your own AI build costs either if you ever scale up beyond the Dual A4500’s. :grimacing:

LOCAL AI or None!

2

NVIDIA is promising something very important: Hugging Face will remain open, developers will still be able to choose models, frameworks, clouds, inference providers and compute platforms, and NVIDIA hardware will not be required to use Hugging Face. That’s NVIDIA’s explicit commitment today.

ChatGPT Image Sep 13, 2026, 07_39_52 PM
ChatGPT Image Sep 13, 2026, 07_39_52 PM1536Ă—1024 643 KB

From my Linked In article
I’ve been watching NVIDIA’s expanding role in the AI ecosystem with a lot of interest — and, increasingly, some concern.

My perspective is a little different because I’m building on both sides of this equation.

On one side, I’m developing hardware platforms that combine embedded processors, sensors, wireless connectivity, edge intelligence and AI.

On the other, I’m building an autonomous stack of AI agents backed by local AI infrastructure — including NVIDIA GPUs — with the ability to also reach outside models and services when appropriate.

That makes one question increasingly important to me:

How much of the AI stack should any one company control?

NVIDIA has built extraordinary technology. CUDA changed accelerated computing. Their GPUs have become fundamental to modern AI, and technologies such as Jetson, Isaac, NVLink and their broader software ecosystem are genuinely impressive.

But look at the direction the industry is moving.

GPU hardware.

CUDA and the software stack.

AI infrastructure.

High-speed interconnects.

Robotics and Physical AI.

CPU partnerships.

Model ecosystems and developer platforms.

Individually, these developments can be great for developers. Together, they create an increasingly vertically integrated AI ecosystem.

For large companies with enormous infrastructure budgets, that may be perfectly acceptable.

For makers, inventors, startups and independent developers, I think we should be paying very close attention.

I’ve already encountered something that every hardware developer eventually learns:

Just because the silicon is technically capable of doing something doesn’t necessarily mean the product tier, software stack, licensing or ecosystem will allow you to use that capability the way you want.

That distinction matters.

My approach going forward is therefore pretty simple:

Use NVIDIA technology. Don’t architect yourself into requiring NVIDIA technology.

My AI agents shouldn’t need to know whether their inference is coming from an NVIDIA GPU, AMD GPU, Intel accelerator, CPU, cloud model or some piece of hardware that hasn’t been invented yet.

They should talk to an abstraction layer through standard APIs.

Likewise, my embedded hardware shouldn’t become unnecessarily dependent on a proprietary AI ecosystem.

CUDA can be an accelerator underneath my architecture.

CUDA should not become my architecture.

The same applies to models. If an open-weight model is important to a project, I want the ability to keep it locally, run it locally and migrate it to another inference platform.

I think this is going to become an increasingly important design principle for anyone building autonomous AI systems.

We spent decades learning the value of open interfaces, interchangeable hardware, standard networking and avoiding unnecessary vendor lock-in.

AI shouldn’t make us forget those lessons.

The technology NVIDIA is producing is remarkable.

But as someone actually building hardware and autonomous AI systems, I want to remain the owner of the architecture sitting above it.

Use the ecosystem. Learn from it. Take advantage of it.

Just make sure you still have an exit door.

ai #ArtificialIntelligence nvidia #EdgeAI #AgenticAI #AutonomousAgents #Hardware #EmbeddedSystems #Robotics #LocalAI #OpenSource #Makers #Innovation

Makers are getting squeezed bad , the cost to entry is Too High. and Nvidia is partly to blame Like it or Not. :v:

HTH
GL :slight_smile: PJ :+1:

3

Hi there,

So, If I go to Schengen can I fill my suitcase with ram (Chinese ram) probably better than what’s made in Idaho… I have been inside Micron there in Boise and Pocatello.
if the unions didn’t suck SO bad it may have been more profitable. I compare it to the work ethic I saw in Samsung M1000 FAB in Soul Korea. for a nice benchmark I’ll throw Parker Hannifin in there in Ireland and equally Hyundai Electronics (which became Hynix and is today SK hynix) they all smoke anything but TSMC and Intel, maybe HP in Oregon.
Fujitsu was a cluster Fuzz same in Texas, total trainwreck.
but I digress…
:grin:

So I Ask how is this possible.

We are only getting half the story from our Government here in America so it would seem, is my opinion.

I’ll repeat its not the AI that will kill humankind, it’s the wasteful use of the natural resources like WATER , yea water H2O, that everyone takes for granted in all the developed countries. The poorest don’t have it so they don’t see it, we do.

If we build so many AI data centers and use all the water , we all go extinct. those are the DOTS I connect, it’s not So & SO Company or Nvidia is the enemy, with there out of control bio weapon building robot, c’mon with that. NO,NO. that’s free market capitalism , like it or Not. Monopolies exist and come and go.

Our resources are finite.

As You were… :saluting_face:
GL :slight_smile: PJ :v:

4

Hi there, This Just in , If you are keeping score. (my agents are , 24x7x365)
:grin:

JUST IN

:warning: NVIDIA–Hugging Face Watch: Maker Access Alert

October 15, 2026 | Confirmed change

We’ve identified a meaningful development affecting independent AI developers.

Hugging Face has eliminated the $0.10 monthly inference credit previously provided to free accounts.

The change was documented by October 8, and developers raised concerns in the Hugging Face community forum on October 9. Blog Picasso IA

What changed?

  • Free accounts: Previously received $0.10 monthly inference credit. Now receive none.
  • PRO accounts: $9/month, including $2 in monthly compute credits.
  • Free model downloads: No newly identified restriction.
  • Hardware neutrality: No new NVIDIA-only requirement identified.
  • LeRobot: No newly identified access restriction.

Hugging Face’s official inference pricing documentation confirms that free users must purchase credits or upgrade to PRO to use its hosted Inference Providers. Hugging Face

Why this matters

The dollar amount is insignificant. The policy change is not.

Previously, a maker could experiment with hosted inference without committing money. That small entry point has disappeared.

Importantly, there is no evidence establishing that NVIDIA directed this decision or that it resulted from the proposed acquisition.

What this means for your AI infrastructure

Your approach of running local inference on T-SHIP, with autonomous agents accessing models through standard APIs, remains sound.

This change reinforces the value of maintaining your own inference capabilities rather than depending entirely on hosted developer services.

Assessment: :yellow_circle: Yellow flag — confirmed reduction in free developer access, but not evidence of NVIDIA-specific hardware lock-in.

The broader NVIDIA–Hugging Face watch remains active.

As you were… :saluting_face:

GL :slight_smile: PJ :v:

5

Hi there,

RIP-OFF ALERT!

7k for something that will be out dated in a year!

Now we have the meaningless mix of Gaming PC with a purported AI PC, give me a BREAK… You Clowns at Nvidia have NO idea what the people want and Need…
it’s not a Leather Jacket or Greed ?

Don’t buy one of these…

You only need to look here to see the truth with this nonsense…

Build your own for less than 3K, 40 Gig VRAM… :grin: :+1:

As you were… :saluting_face:

GL :slight_smile: PJ :v: