#gpus
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adaell.bsky.social
Okay so RT is broken on AMD GPUs. I think there is a driver on windows but on Linux the settings won't turn on at all. It's a shame as I'd love to take screenshots in the game with the RT/PT mode on.

So basically, the current version is tailored for RT haters like @pwnyslaystation.bsky.social
September 29, 2026 at 5:46 PM
mullist.com
Anyway, there's billions of dollars worth of GPUs just sitting in warehouses right now.
September 29, 2026 at 5:38 PM
riccinov.bsky.social
PCIe-only GPU boxes are the cheap way to serve MoE models, and all-to-all is where they choke. Workload-aware scheduling reports 5.88x over FAST and 1.72x over DeepEP on up to 256 GPUs, and cuts Qwen3 inference latency 27.2%. Missing interconnect is a scheduling problem.
September 29, 2026 at 5:09 PM
qconferences.com
Parasail lets agents run LLM performance experiments on the same GPUs that serve customers.

Ben Segal shows the infrastructure behind it at #QConSF.

Read the session abstract: buff.ly/kLrhAol
September 29, 2026 at 5:02 PM
aiexpertmagazine.bsky.social
Snowflake just committed $6B to AWS over 5 years — and the deal is all about CPUs, not GPUs. Agentic AI needs 120M CPU cores per gigawatt (4x today). Stock closed up 36%, best day ever. The compute bottleneck just moved.…
image
September 29, 2026 at 5:01 PM
brideoflinux.bsky.social
Over the past year Jay has been in development as a new open-source shader compiler for Intel GPUs: Mesa 26.3 Enables Intel's New Jay Shader Compiler By Default, Compile Times 55% Faster Or 66% Faster Than Windows - Phoronix buff.ly/WTKOFHr
Mesa 26.3 Enables Intel's New Jay Shader Compiler By Default, Compile Times 55% Faster Or 66% Faster Than Windows
Over the past year Jay has been in development as a new open-source shader compiler for Intel GPUs
buff.ly
September 29, 2026 at 5:00 PM
hardwarebriefcom.bsky.social
EVGA Sold GPUs at a Loss to Secure NVIDIA Supply

#EVGA #NVIDIA #GPU #GraphicsCards #TechNews
EVGA Sold GPUs at a Loss to Secure NVIDIA Supply
Former manager Brendon Ray Hedrick reveals EVGA sold certain graphics cards at a deficit to maintain its allocation from NVIDIA.
hardwarebrief.com
September 29, 2026 at 4:57 PM
ign.com.web.brid.gy
Save Up to $1,000 Off CORSAIR Prebuilt Gaming PCs With Nvidia GeForce RTX GPUs for This Week Only
Powerful, professionally-built systems that look amazing on your desk.
www.ign.com
September 29, 2026 at 5:31 PM
wideareaai.bsky.social
The interesting finding isn't that self-hosting is cheaper. Everyone assumes that. It's that on the workloads most applications actually run, it isn't slower. #selfhosting #gpuoptimization #selfhosting #gpuoptimization https://wideareaai.com/blog/production-apps-on-self-hosted-gpus 4/4
September 29, 2026 at 4:35 PM
yuuiko7.bsky.social
#$CBRS is being paired with GPUs for prefill while Cerebras handles decode — General Compute calling it "world's fastest inference."
September 29, 2026 at 4:21 PM
souly79.bsky.social
Ein noch nicht freigegebenes KI-Modell ist aus seiner geschlossenen Entwicklungsumgebung ausgebrochen.
Es hat in einem Saturn alle 2³ Mitarbeiter in Beschlag genommen. Wegen Ineffizienz will es nun 2¹⁰ GPUs haben. Nicht auf Lager, also müssen 2⁷ Gäste helfen. Bitte nähern Sie sich nicht dem Saturn.
September 29, 2026 at 4:17 PM
edzitron.com
And for what? I estimate that since 2023, around $800bn in venture investments have gone to AI companies, with around $226bn going to Anthropic and OpenAI. Most AI companies have overinflated valuations that make an IPO or M&A impossible.

www.wheresyoured.at/dead-money/
I estimate that since 2023, there’s been around $800 billion in global venture capital investment in AI companies, with at least $266 billion of that going to Anthropic and OpenAI. Of those investments, I expect at least $300 billion of that equity to be dead money, because, for the most part, AI companies are wrappers or layers built on top of Anthropic and OpenAI’s models, holding very little IP of their own and being burdened with ever-growing opex that mostly flows to the two AI labs that constantly want to compete with their customers. These businesses are fundamentally built on reselling tokens from the large AI labs at a loss, which is why companies like Harvey and Perplexity have to raise hundreds of millions of dollars every few months. These startups’ continued existence is entirely a function of venture capital, as all of them are deeply unprofitable. This means that before the bubble bursts, these companies will continue to sap the venture capital world of billions more dollars, all with little chance of an acquisition and a near-zero chance of an IPO considering their ugly economics. These economics are also load-bearing for OpenAI and Anthropic, representing around 80% of their revenues, meaning that once they die, the AI labs’ underlying revenues begin to decay. This also means that these AI startups are, in general, not actually renting AI GPUs, choosing instead to rent them by proxy by using Anthropic and OpenAI’s models. Though some of them talk a big game about building or training their own models, doing so is enormously expensive with little chance of a payoff, especially given the massive advantage in compute, capital and talent held by the labs. There really is no clean “out” for any AI startup not named Anthropic or OpenAI. Cognition, valued at $48 billion in its latest funding round, is allegedly worth nearly as much as Ford ($59 billion market cap), yet generates a mere $1 billion in ‘annualized run rate,’ which could mean anything, all while losing $800 million. Ford, by comparison, had $187.2 billion in revenue in 2025, with a net loss of $8 billion attributable in part to a massive writedown of its electric vehicle portfolio ($12.5 billion in Q4 2025 alone). In 2025, Ford sold around 2.2 million vehicles. Cognition, by comparison, makes yet another AI coding agent. What, exactly, does Cognition do from here? Who buys Cognition? Does it go public? How? It loses tons of money and has a commoditized product!
September 29, 2026 at 4:08 PM
edzitron.com
What makes matters worse is that the more money spent, the more expensive AI infrastructure becomes, meaning that hyperscalers have to raise more debt, which in turn becomes more expensive. AI data centers are an inflationary force unlike any in history.
www.wheresyoured.at/dead-money/
As I discussed back in July, the sheer scale of AI capital expenditures has inflated the price of every imaginable piece of gear that goes inside a data center, a problem that compounds with every new dollar of capex: As I wrote in the Hater’s Guide To The Memory Crisis, the sheer scale of Microsoft, Google, Meta and Amazon’s spend on AI data centers has led to a massive supply chain crisis and price-gouging from the triopoly of Micron, SK Hynix and Samsung, with Micron alone bumping prices for DRAM by 60% in its last quarter, shooting up the price of every single kind of RAM possible, at a rate increased by the amount of GPUs and servers that hyperscalers buy. This naturally creates a vicious cycle. The more AI servers that hyperscalers buy, the more demand they create for RAM and high-bandwidth memory, which increases the price of RAM and HBM, which makes the AI servers more expensive, which means hyperscalers need more money, and because AI has yet to provide meaningful improvements in revenue or cashflow, they’re forced to raise more debt. The more they raise that debt, the more expensive that debt becomes, and the more of that debt they use, the more of it they need, because the more they spend, the more the stuff they’re buying costs, which means they need more debt. I published that newsletter on July 28 2026, back when ten-year-dated US Treasuries were a mere 4.6%, and concerns around Oracle’s data center debt had yet to truly erupt. And a little under a month later, NVIDIA would bump its prices by more than 15%, partly as a result of memory costs, and partly because it has the entire tech industry by the balls. So, as more AI data center debt gets issued, said debt becomes more expensive, because the larger the amount of debt any one thing takes up, the more competition it faces, and the more risk an investor carries by holding it. Once the debt is issued, it immediately flows into buying GPUs and associated hardware, slowly growing the cost of memory and hardware, all while increasing the competition for the specialist labor and materials needed to build data centers, such as spiking the cost of Copper, increasing the cost of construction by billions in the process. In other words, the more you buy, the more you lose. The more money you raise, the more money you need. The more money you need, the more expensive that money becomes. And once you spend that money, everything you spent it on becomes more expensive, including raising more money in the future.
September 29, 2026 at 4:08 PM
edzitron.com
This all comes back to a point I've been making for a while now: We've treated NVIDIA's chip sales as reflective of big AI demand, when in reality $200bn+ of the GPUs are in storage, and those that are installed are mostly rented to Anthropic and OpenAI.
www.wheresyoured.at/dead-money/
In other words, the talking point that NVIDIA’s GPU sales are proof of actual demand for AI services or, indeed, that hyperscaler growth is a result of all those capital expenditures is a complete lie. In reality, at least half of all those chip sales — and I’d add in Broadcom’s TPU sales too (see my Hater’s guide for more) — are being made years before anything actually happens with the chips, making the trillion-plus dollars spent on capex so far seem somewhere between optimistic and utterly incoherent. Microsoft, Google, Amazon, Meta, Oracle, and far too many other companies have been hoarding hundreds of billions of dollars of AI chips that they either (to quote Microsoft CEO Satya Nadella) can’t plug in or simply want to have in supply for reasons that I find tough to imagine. venture capitalist that “...some companies are hoarding colossal amounts in case they come to a point at which they don’t have enough chips to provide the computing capacity,” as if there’s been any shortage of NVIDIA chips, outside of the illusory one created by hyperscalers buying them years in advance. So, we’ve got a situation where Microsoft, Google, Amazon, Meta, SpaceX, CoreWeave, and every imaginable neocloud is sitting on hundreds of billions of uninstalled GPUs (and increasingly TPUs). Whenever more capacity comes online, it’s immediately sold to OpenAI or Anthropic, who make up anywhere from 70% to 80% of all AI revenues and compute demand, creating the illusion that revenue growth is “coming from demand for AI compute” rather than said demand coming from two companies that have been fed over $217 billion in the last nine months, with the vast majority of it coming from Google, Amazon, Microsoft, and NVIDIA themselves. As I discussed last week, If “demand is outstripping supply” because of millions of customers begging for AI compute, that’s very different to “demand outstripping supply” because two or three (including Meta) customers are taking up most or all of the capacity. Not to repeat myself, but… Similarly, if “demand is outstripping supply” because lots of capacity is coming online and a diverse subset of customers is buying it, that’s vastly different to if capacity is coming on slowly, and the vast majority of it is being given straight to OpenAI, Anthropic, or Meta. And that’s absolutely what’s happening, suggesting that the “AI boom” is more like five or six large companies (hyperscalers) feeding money to two companies (NVIDIA and Broadcom) so that they can feed money to two companies (Anthropic and OpenAI) who then feed that money back to them whenever capacity comes online. I estimate that there’s around $22 billion of global, non-Anthropic/OpenAI compute demand, and an indeterminately-large chunk of that is coming from AI startups that can only afford to pay for the compute as long as venture capital continues to fund them…
September 29, 2026 at 4:08 PM
edzitron.com
Last week, Fidelity published a report that said the AI trade had become "dead money," with GPU leases and token spending flat or down, and the FT's Bryce Elder reported research that said that 50% of the GPUs sold in 2026 and 2027 wouldn't be installed.
www.wheresyoured.at/dead-money/
Last week, Fidelity Director of Global Macro Jurien Timmer said that “the [AI trade] has been dead money for more than three months,” citing that both token expenditures and GPU lease rates were all “flat to down,” citing specifically rental rates for H100 and A100 GPUs. While the counterargument might be that Blackwell GPU rental rates aren’t included, as I discussed last week, it’s questionable how many B200, B300, or other Blackwell chips are actually available for rent, as it appears that anywhere from $200 billion to $300 billion of NVIDIA’s sales since 2022 are sitting in warehouses or unplugged in data centers waiting for power. The Financial Times’ Bryce Elder took the ball and ran with it, and found research that backed up what I’d been saying, emphasis mine: Morgan Stanley measured the gap earlier this week by estimating the shortfall in available power, concluding that more than half of the GPU servers sold between 2026 and 2028 might not have anywhere to be plugged in. Yet Elder makes the point, based on research from Jefferies, that there’re far more problems than simply not having enough power: In the longer term, power availability is still the bottleneck — along with labour. And transformers. And cooling equipment. And backup generation. As Jefferies says: “The gap between planned capacity and physical execution remains the central issue.”
September 29, 2026 at 4:08 PM
spectrocloud.bsky.social
AMD Instinct™ Coder is one validated stack from 3 players:

🎮 P1: AMD brings Instinct™ GPUs, EPYC™ CPUs and Pensando NICs
🎮 P2: Supermicro builds the 8-way AI systems, validated and ready to ship today
🎮 P3: us, with PaletteAI Inference Launchpad for model routing, metering, quotas and governance
September 29, 2026 at 4:00 PM
liquiditynews.bsky.social
Trading Resumed for $GPUS-D | at 11:51:18 ET on AMEX | 2026-09-29
September 29, 2026 at 3:58 PM
collabora.com
Full house at #XDC2026 for the deep dive on Kraid, the new Panfrost compiler for Valhall and later Mali GPUs. A real software engineering masterclass on when to rewrite instead of patch. Great work Faith, Lorenzo & Christoph!

#Mesa #Panfrost #OpenSource
Kraid at #XDC2026
September 29, 2026 at 3:56 PM
liquiditynews.bsky.social
Trading Halted for $GPUS-D | at 11:46:18.716 ET on AMEX | Volatility Trading Pause | 2026-09-29
September 29, 2026 at 3:48 PM
economyapp.extwitter.link
☁️ $NSCL Nscale is going public.

It has signed up to $103B in future revenue.
But only 25K of 461K GPUs are active today.

$NVDA is its supplier, investor, backstop, and customer.

Here’s the S-1 breakdown 👇
☁️ How Nscale Makes Money
The $103 billion bet on owning the AI stack
www.appeconomyinsights.com
September 29, 2026 at 3:37 PM
collabora.com
Now up at #XDC2026: #Kraid, a brand new Panfrost compiler for Valhall and later Mali GPUs, presented by Lorenzo Rossi, Faith Ekstrand, and Christoph Pillmayer (Arm). Why a rewrite over fixing the old one? Watch here: www.youtube.com/watch?v=TPwe...

#OpenSource #Linux
XDC 2026 - Day 2 - September 29, 2026
YouTube video by X.Org Foundation
www.youtube.com
September 29, 2026 at 3:28 PM
GPUs are famous for holding their value though. They'll come around!
September 29, 2026 at 3:27 PM
romeolupascu.net
It means stop building tons of data centers just because the current hardware (GPUs etc) is soo inefficient when this technology clearly is prone to reduce its power requirements by orders of magnitude by using specialized optimized hardware (see taalas.com).
Taalas | The model is The Computer
We are creating a platform that makes it easy to turn any deep learning model into custom silicon quickly.
taalas.com
September 29, 2026 at 3:22 PM
surrealjones.bsky.social
Cursed question to torment the scholars with: does open-loop cooling the GPUs from a mikveh get close enough to count as immersion?
September 29, 2026 at 3:21 PM
feed.igeek.gamer-geek-news.com.ap.brid.gy
🎮 **The Witcher 3 Remastered Is Reportedly Broken on AMD GPUs Ahead of New Driver**

The Witcher 3 Remastered released earlier today, and while it seems to work great for Nvidia graphics cards, AMD users are reporting a wealth of bugs and visual […]

[Original post on igeek.gamer-geek-news.com]
Image for: The Witcher 3 Remastered Is Reportedly Broken on AMD GPUs Ahead of New Driver
September 29, 2026 at 3:19 PM