DGX

phaskellhall

Yes this is where the future is going IMO. Apple is already planning for this with the Apple Mac Studio. Nvidia is doing the same with the DGX spark. Both are in the $4-10k range and are aimed at running local models that require less compute which can then be integrated with a final frontier cloud model for anything it can’t handle. It’s one reason I think Nvidia can still grow even after companies like Open Ai and Anthropic start to hit a wall.

Rycross

Case in point. Look, my post was not about frontier models or whatever, but about GPU demand. I kinda don't give a flying fuck if demand is moving from Anthropic to whatever in this discussion. Your article cites providers like OpenRouter, and they're routing to models hosted on *drumrolls* data centers. Yes, you *can* self-host open models. Most do not. Even if you're self-hosting, you still need GPUs or equivalent to do that self-hosting, which you'll likely be renting unless you're so small scale you can stick a rack in the closet. Most companies in that article are not going to be plugging a bunch of DGX Sparks running quantized versions of DeepSeek or Qwen.

Pimpwerx

Hardware prices suggest that there are a shit ton of others. I wouldn’t mind if demand disappeared. I’d buy a DGX Spark or 128GB Mac. But alas, prices are the highest they’ve ever been. We’ve even eclipsed 90s prices.

Equivalent_Sea_236

43mg snus while stroking my half soft 2 incher while watching tranny corn and MU going to 0 on my DGX spark, life is good.

GloomyMembership4197

Breaking: NVIDIA announced DGX POD. AI factory buildout goes from 4 months to 1 month.

GloomyMembership4197

NVIDIA DGX POD is a SMCI killer? SMCI was not mentioned in producers... I'm concerned...

BlackBugs

Start playing around with Large Language Models! Go get 2 DGX Sparks. Wouldn’t it be cool to understand and play with the technology that’s changing the future?

InterestOk9770

6 months is a very long time! Companies are decreasing their reliance on Anthropic models. Microsoft and Meta being their biggest customers just recently cut their Anthropic spending. Meta is using its own models now and Microsoft has started hosting Chinese models. This will significantly affect their future profits. People have realised now that you don't need Fable or Astra for everything. Everyone has started using flash models for actual coding and frontier models only for planning. With the world now moving towards efficient and cheaper inference and flash models being good enough for 98% of the tasks, it's gonna be very had for Anthropic to maintain it's current revenue, considering all other additional things going around, i.e. competitors propping up! Also with the current Qwen 3.8 Flash next being good for most of the task and any company could host it and use it for basically peanuts, the future is pretty grime. The effect of this would appear in next quarters revenue for Anthropic. 2026 is the year Anthropic was able to charge $25 per million tokens and make everyone believe that API pricing is real thing and more AI usage and spending billions on tokens meant AGI. I belive this is why they're pushing the IPO now before next quarters revenue get disclosed. I just can't justify this company's valuation when I can now locally run QWEN 3.8 Flash on a single DGX spark continuously 24/7 and support it with a $20 CLaude / OAI subscription if when I need planning. The market for API pricing is gonna go extinct if they don't scale down their API prices, which if they do, their revenues are gonna drop too.

SanDisk_Made_Me_Rich

AMD (Strix/Gorgon Halo desktops and laptops), Apple (M5/M6 Minis and Studios, and Macbooks), and Nvidia (DGX Spark, RTX Spark desktops and laptops) already have their Plan Bs well in motion. If data centers stop needing as much I *am* their new data centers to supply. The Chip Maker Mafia™ will always be the source of the problem ready to take advantage of whoever is next.

pineapplekiwipen

DGX spark 6999 now and they have a 64gb version out for the old price lmao

maester_t

>And that will be $5,000 a shovel Almost literally. I was just searching a couple hours ago and saw that Nvidia's DGX Spark (personal desktop aimed at specifically running powerful AI models) is currently selling for about $5500.

heretogetpwned

That switch requires businesses that are also trying to get away from CapEx to get back into the on-prem CapEx rotation again. Since all equipment is overpriced due to DRAM and NAND constraints it's not an easy sell. Orgs using MCP or local agents to drive automation really benefit from bringing a local model in house you have control over and contingencies for. If Claude is down, your automation is down until cloudlfare or whomever bounced the internet that day fixes their shit. It costs $60/hr to host an older GPU Server on Azure and $7k - $10k for a Mac Studio. DGX Spark $6k. Keeping my eyes on the security products and other optimization middleware. The models are getting tuned, the hardware is getting tuned, now the enterprise software has to get tuned for AI ingest or coders building skills to clean up or sanitize the ingest.

Turtlesaur

I used my NVDA winnings to buy a DGX Spark. Thanks Jenson

greenpride32

They did explore that path, it was called DGX Cloud. Problem was it put them in direct competition with their biggest buyers. So you can either keep producing insane profits doing what you've been expert in doing for decades, or completely shift your business from asset light to asset heavy in a competitive field already dominated by big players. And by consuming some of your own GPU's, you are no longer getting massive sales and profit upfront, you are hoping some will pay off in the longer run. While you own the key resource today, you only encourage wider adoption of AMD tomorrow. Every move NVDA is making now is to lock in their GPU's and ecosystem; I like it. Take the largest open source platform, and lock them into NVDA hardware. Partner with countries to run their sovereign platforms on NVDA. Provide financing to smaller players who otherwise couldn't afford AI compute infrastucture; future success is then shared.

Pessimistic93

If the DGX B200 was its own company it would be bigger than Nike, McDonalds and Nvidia combined.

GlokzDNB

I rephrased your post into questions, because its very chaotic then AI answered these questions 1. **Why not lever up infinitely if payback is 1–2 years?** Because the quoted 1–2 year “payback” is usually **GPU/compute economics at high utilization**, not guaranteed cash payback on the entire data-center project. Debt providers care about contracted revenue, utilization risk, GPU obsolescence, construction timelines and residual values. You can't responsibly finance uncertain future demand at 80–90% LTV just because spot compute economics look great. 2. **Why do Nebius/IREN issue equity?** Because they are trying to grow **faster than their debt capacity**. If you can invest $1 and plausibly create substantially more than $1 of enterprise value, issuing expensive equity to fund additional projects can actually be accretive. Equity issuance is only bearish if the return on the new capital is below the dilution cost. NVIDIA itself just invested **$2bn into Nebius** while Nebius plans >5 GW of NVIDIA systems by 2030. 3. **Why doesn't NVIDIA just own all the compute?** Because NVIDIA already captures an extraordinarily high-margin part of the value chain **without taking construction, power, financing and utilization risk**. Becoming CoreWeave/Nebius would require enormous capital and would compete directly with NVIDIA's own customers. NVIDIA instead operates DGX Cloud *through* CSPs/NVIDIA Cloud Partners and is now even using revenue-sharing/credit support to accelerate third-party infrastructure. 4. **Why do hyperscalers lease instead of own?** They actually **do both**. Leasing buys **speed, power access, geographic capacity and flexibility**. If CoreWeave can deliver a powered GPU cluster in 12 months while Microsoft building it itself takes 24–36 months, paying someone else's return can be economically rational because the opportunity cost of waiting is enormous. Microsoft, AWS and Google simultaneously build their own infrastructure and consume third-party capacity. NVIDIA lists AWS, Google, Microsoft, Oracle plus CoreWeave, Nebius and others as Rubin deployment partners. AI Bubble is only possible if demand collapses. You can't even comprehend how important AI race is for US gov. Its cold war 2.0, including cybersecurity, technological dominance, rapid growth of new technology across the table. You only look at costs and money spent - have you ever asked yourself a question - what it would cost US in a 50 years horizon losing AI to China ? Then try to find appropriate price tag for that. The answer is - look at British Empire collapse, how much it cost them to lose global dominance to US?

Jean_Jones_666

what a lovely marriage of ignorance and smugness, the TRUE (ah the irony) cost of LLMs is that you can ALREADFY run them locally and it is MORE than justified, check DGX spark, just run open source on them you didn't check SemiAnalysis either, just referred to unknown reddit "experts" lol, now that's very convincing, i take it you're both too stupid to read, or to not use anecdotal evidence

seinberg

Yes, fair. I do think as things like the DGX Spark w/so much unified memory along with super capable smaller models it might also start to erode even the cloud providers margins. For $8k (2 sparks) and with the right skills you can locally run models that are as good or better than Opus 4.6 - only 8 months after it blew the doors off the enterprise. Using a regular 110v household outlet where you don't need to care about utilization. Problem is it's still a hobbyist thing and requires you to know too much about setting up and building a GPU cluster etc to be more than a niche, but as all that becomes commodotized and Moore's law improves computing density and models naturally compress there's a potential world where most of this is done locally at high quality and AI isn't even a cloud thing anymore.

Acceptable-Article28

Web search index takes a huge amount on infrastructure.  Hosting a frontier level AI model is essentially a rack of GPU's.  Nvidia already sells that, it's a DGX.  The latest Kimi model is pretty good.  One thing though is that if you can batch requests the computation becomes more efficient.  If the organization is large enough there will be enough batching going on though.  Openrouter does this for the little guy, so you don't need your own DGX. 

Necropaws

You don't have enough space to host your own web search index. But you can buy hardware to host your own LLM. A DGX Spark will cost you around $5k-$6k and would be sufficient for a family. For this money you can get around 300TB of HDD with the current prices without any computer attached. Good luck fitting a web search index into it. In 2 to 3 years, when the next generation of AI compute will be present, the price will be either lower and/or the performance better. Frontier models have currently around 6 months before open models reach an equal sophistication. There will be a tiered market. People using frontier models, people using open models hosted by a third party and people hosting their own models. There will be a market for frontier model companies, but not in the trillions of dollars as AI will become a commodity like air travel. Most users will pay cents for AI models and be happy about it.

frugaleringenieur

These model sizes are not feasible to individual people and small businesses, yes. That's why I mentioned medium size models in the 80-150B param range. You need at least a DGX B300 however, for Kimi K3 and similar. This is an investment of $500k and more. However, the space still accelerates and we are looking toward K3 intelligence in 500B next, then 250B, then 100B and it's feasible again for small businesses.

Keeltoodeep

I have a large stake in NVDA. Mix of luck and believing in Jensen’s keynote speech in 2016 where he introduced the DGX-1 and claimed** **it would revolutionize compute. Really turned me into a believer. This year, my wife and I surpassed $1M in net worth so I guess I’m technically a millionaire now. Anyway, I am worried that jf OpenAI crashes it will take down the AI hardware stocks with it.

aperartnft

Good point on the recurring revenue angle and it does feel like Nvidia getting the DGX Cloud outcome it wanted, just through partners instead of building it themselves. Sharon AI raised $1.6B privately in June plus IPO'd in February and Firmus is projecting $25-30B in actual signed customer commitments over six years, so there's genuine outside capital and end-demand in the mix. One thing worth flagging though, Nvidia is also providing a buyback guarantee on unsold capacity, so if these partners can't fill their datacenters, Nvidia eats the utilization risk. The revenue-share strategy is genuinely smart and different from pure circularity, but Nvidia's still holding some of the downside.

greenpride32

This is actually very good for NVDA. As they get a cut of the services sold it will create a recurring revenue stream, something they are sorely lacking and now filling the void of what DGX Cloud could have been. NVDA sells GPU rack servers to the big hyperscalers and the smaller neo clouds. Those companies then wrap it up under their own proprietary software stacks and sell the product to create their own margin. Imagine if NVDA took a slice of all those sales. Well that's what will be happening with these smaller players. But NVDA doesn't bear the cost of building and maintaining athe datacenter, or selling the product. Sharon AI recently raised $1.6b and Firmus did $500m. That's not circular financing, it's capital injection.

Objective-Food-9996

Yes they can, there's many other obstacles, but a single starlink sat can house a full rack of DGX hardware and cool it. The idea never was to deploy full datacenters as satellites, like a ISS full with racks, but even then they could do it - cooling wise. There's enough pitfalls to the idea of space inference and training, but radiative cooling is not a major one anymore.

Eclectic_Beer_Tap

DGX sparks are ass

Antiwhippy

So digesting the NVIDIA computex news, all they did was put the DGX sparks chips that didn't sell because they were too expensive for what they were... into an even more expensive body?

tke248

Good one. $PENG probably fits the AI infrastructure / managed AI factory bucket. Not a direct Jensen name from the GTC table, but they’re tied to NVIDIA DGX clusters, AI/HPC infrastructure, and memory. Small enough that the shovel narrative can actually move it. I’d probably grade it B+ as a second-order shovel.

GloomyMembership4197

NVIDIA with Microsoft making PC revolution not seen in 40 years since first PC: NVIDIA DGX Spark!!!!!!

nanobot_1000

$4K+ like DGX Spark

nanobot_1000

It's already been available for a year as mini PC and is called DGX Spark. Welcome to the "new era" of ageantic marketing

kra73ace

That's capitalism in action. The shift in AI is moving capital around. It's violent and it overshoots. Doesn't automatically mean it's irrational, the future is a range of outcomes. Maybe memory is no longer cyclical (I sold micron at $300). Maybe Dell will sell Nvidia DGX Sparks (someone leaked it). It's a dangerous ride no doubt.

Herbrax212

it started with a spark : DGX SPARK MICROSOFT EDITION INCOMING

nanobot_1000

It's N1X, same as DGX Spark

perceptive_AI

I asked claude to compare Nvidia's DGX AI workstation ($97k) and Corsair's maxxed out version ($78k) and DGX have 11x more AI compute for 24% more.

pineapplekiwipen

yeah looks like nvidia DGX reskins

eberkut

Schneider Electric is in the Bill of Material of Nvidia DGX.

Ok-Blood4340

A single DGX B200 cluster (8 B200 chips) puts out about 14.3kW. A single rack, has about 200 B200, requires 350kW for just the server. The ISS may be 30 year old tech, but the current tech to remove heat from a chip is the same then as it is now. Like, what technology is better to remove heat from CPUs and GPUs that the ISS isn’t using? Computers use the same technology, fans, heat sinks, maybe water cooled loops at best. Unless now we’re shipping liquid NO2 several times a day and using copper pot? Also, the ISS is the single most expensive, greatest construction of modern history. It can dissipate the same heat as half a B200 server. AI warehouses have 100s, 1000s of servers.

wtjones

I was wrong on both accounts. Huawei is seeing 20-40% yields at 7 nm. So for every Ascend that Huawei sells, China has to subsidize 2/3 of the cost. It takes 10 Ascend 950s to equal one B300. I’m estimating on the low side here to be fair. To have equivalent compute, you’re going to need 10x the number of nodes and China is going to have to subsidize 2/3 of that for fab waste. 10x the nodes come with 10x the space and 5x the power draw. The total ecosystem cost for a single NVIDIA HGX/DGX, with three years of data center, power, etc is in the $650,000 range, if we go with the highest possible values. For a Chinese company to buy equivalent compute from Huawei, data center, power, etc. it’s going to cost them $2,560,000. The Chinese government is going to pay an additional $1,720,000 in subsidies to Huawei to make up for the inefficient chip making process. So SpaceX pays $650,000 and ByteDance and the Chinese government pay $4,280,000 for equivalent compute. Hard to compete at that rate.

vejiki-pjojok

I realized that. I also realized that the 512 GB Mac Studio models are no longer offered. So there are cases where the 4x DGX Spark might be better, due to same memory as 2x 256 GB Mac Studios, but with CUDA acceleration, so, doesn't have to rely on an MLX model.

hishazelglance

Sorry - when I said DGX I was referring to the Spark.

vejiki-pjojok

The DGX B200 is $500k. DGX Spark can't fit larger models, is faster in throughput than Mac Minis, but is vastly more expensive. It's not really a better deal, although it's pretty good. Also, because of its poor bandwidth, you can't actually train on it, so it's the same as a Mac Mini in that regard.

hishazelglance

Disagree, Nvidia DGX stacked is the best financial option for inference. That or the new AMD competitor to the DGX

UpsetKoalaBear

1. It uses LPDDR4 or LPDDR5. If you want the highest level of performance, LPDDR5 would be the option which is 10nm and uses EUV. LPDDR4 is an order of magnitude slower than LPDDR5 it is not even a comparison. 2. This still matters because there is an upper limit to how much bandwidth you can squeeze out of LPDDR5 and it is still lower than HBM. This means, with the rate of AI acceleration, you would eventually reach a point where the bandwidth cannot keep up and a brand new chip would have to be made. All of these inference chips have the same issue. They are really good for a subset of AI models right now, but whether they will be in 3-5 years, which is the average deployment, is a different topic entirely. Companies buy cards with HBM because it offers them longevity without having to refit servers. 3. I find this hard to believe. If this is true, then there’s no explanation for how AMD couldn’t do the same with ROCm or Intel with OneAPI. In which case, they have no moat. There would be nothing stopping other companies with deeper pockets from doing that. If anything, this should be a sign of snake oil. There’s no explanation for why AMD/Intel have spent millions of R&D into their own API’s than integrate into CUDA. If it was that simple, it would have already been done. 4. Nvidia already does this and corner the market. It is fundamentally impossible to compete with the DGX ecosystem as they have spent years making it. They have dev machines, API’s, foundation models and the actual hardware. 5. Every AI is compatible with every hardware anyways as long as your hardware is supported by frameworks like PyTorch which allow it to run agnostically. Also, the website bro? Do you think that is reputable? 6. Again, it is using LPDDR4. I can almost guarantee you that, if this shit is ever released, it will perform worse than a normal consumer GPU if it is using LPDDR4. You can’t say I’m flinging insults when your, now removed comment, tried to convince people to ignore any skepticism. You’re inviting criticism.

Merpchud

$DGXX up 30 to 40 percent on the Nasdaq $DGX on the TSX is up 5 percent so far Chance for 30ish percent bump on the tsx coming to catch up with the Nasdaq?

XYHopGuy

I suggest you examine your premise: > was a measly $100 billion to setup and run $100B to setup and run this service for an unfathomably large number of people. For just me? That workload is several orders of magnitude cheaper than $100B in capital costs. At the bare minimum, a frontier model would need something like an 8-way DGX system to run, but those have a lifespan of 5-7 years and costs are amortized by multi-tenancy (much more cost effective than self hosting!) Cloud providers are making hand-over-first in GPU rentals. We see this in google, Microsoft, and amazon earnings quarter after quarter. Second tier providers and neo-clouds are ramping investment and slow to turn a profit, yes, such is the cost of investment. Furthermore, we are seeing demand continue to _increase_ -- this suggests those investments will payoff. GPU spot rental prices have gone UP over the last few months. Party is just getting started. You may find this overview helpful: https://newsletter.semianalysis.com/p/gpu-cloud-economics-explained-the

Rinyaboi

VM DGX beat estimated And earnings and tanked. Why

Rinyaboi

Ok, DGX beat estimated And earnings and tanked. Rip my calls and shares. 🤡 Market.

circuitji

An enterprise-grade 8-GPU H200 server (like a Dell PowerEdge XE9680 or an NVIDIA DGX H200) currently costs between $400,000 and $500,000.

danger_boi

Lenovo market cap 15.8B on the HK exchange, same as super micro. My nipples are hard. DGX spark manufacturers could be a clue, ASUS? HPE too expensive by comparison

V4N0

Yeah makes sense, they already partner with Nvidia for the DGX servers, the motherboards are designed by Super Micro

I_am_Nerman

I bought some DGX. Might go all in.

RaceEcstatic3045

At the Nvidia GTC, Nvidia announced new AI data-center hardware, including the Groq-3 AI inference chip and a new CPU-based server platform designed to power AI infrastructure. The new systems aim to provide a full AI data-center stack (CPU + accelerators + servers), which puts Nvidia in more direct competition with traditional server-CPU providers like Intel. Oh yes and Intel chips are being used mainly in Nvidia’s DGX Rubin NVL8 AI servers, where Xeon 6 CPUs act as the host processor controlling clusters of Rubin GPUs in large AI data-center systems However as said longterm nvidia is coming for them. Plus no fab news.

greenpride32

I personally decided to stay away from memory and storage because historicaly they are a commodity business. Their biggest buyers would be server/desktop/laptop buyers just looking for the cheapest prices to increase their own margin. Now back in the day, I did invest in SNDK, before they got bought out and spun out again. They were the first to successfuly commerical flash, until it became commditized. I still see there being room to run for memory and storage, but I'm a long term investor. I feel there is some risk of the rug being pulled out at some point. These companies are benefiting from high demand, rather than from competitive advantage or innovation. I'd rather park my money in a company such as NVDA where historically it is well run, high margin, innovative; and even if GPU sales slow they are building new revenue streams such as DGX Cloud.

Miserable-Radish915

Huh? They aint buying your bog stand $RU and 2RU servers they buy the DGX SuperPods with a massive Netapp all flash array underneath,

ked913

He provided 60% of funding in 2015, Brockman has an open diary stating about lying and deceiving him. Musk convinced Ilya from Google Brain to OpenAi. He was also the person to convince Huang to give OpenAI to DGX compute. All under the assumption they were a non profit. That law suit is not clear cut. I am not a fan of Musk by any merits but his numbers and evidence seem clear to me.

Dumbest-Questions

> Specifically because Nividia uses silver in its high-end AI GPUs and server boards. LOL, dude. Yes, NVIDIA GPUs uses silver, but not in any economically meaningful quantity. It's used in solders mostly (typically Sn-Ag-Cu alloys) and it is standard across the semiconductor industry, not NVIDIA-specific. Also, small amounts of silver are used in die attach materials, thermal interface materials, and conductive adhesives. Silver is not a core input like silicon, copper, or aluminum. There is no scaling relationship between GPU volume and silver demand that matters at the commodity level. Nor does increase in silver price change the economics of GPU market. Back of envelope check. Assume 5–10 grams of silver per GPU server (this is far, far above reality). Silver at $100/oz ≈ $3/gram, that’s $15 of silver per server. HGX / DGX-class servers sell for $150k–$400k+. NVIDIA’s gross margins are driven by, silicon yield and wafer pricing (TSMC), advanced packaging (CoWoS), HBM stacks and software (CUDA, networking, ecosystem lock-in). Raw material prices are almost inconsequential.

Tentakurusama

Climate impact and surviving as a specie is not grounded to any logic. AI is becoming more and more sustainable (a freaking DGX spark is running on 120Watts peak), more and more ubiquitous and less competitive with clear winniners like Google / Anthropic while companies like OpenAi won't last very long. The rationale is flawed, that's a very risky take.

notkidingatallshutup

DGX!!!! pushing up to 4$

UpsetKoalaBear

They’re being incredibly smart with DGX. The whole goal here is to build an Nvidia ecosystem for AI. The idea is everything just “works” with each other, it’s also clear by some of their frameworks that comes with their products to develop AI models. The end result is you, as a company, no longer need to invest in developing the “low level” minutiae of AI, such as programming CUDA, memory management, etc. You only need to worry about developing a competent model using the frameworks they provide and any inputs you specify. You can see this already with things like the Jetson Thor. You have software like [Groot](https://developer.nvidia.com/isaac/gr00t) and [Cosmos](https://developer.nvidia.com/cosmos) which is a fully fledged framework for developing robotic AI models and helps to handle things like image processing or similar. The downside is vendor lock in. This is probably why we’re seeing Google’s TPU’s being hyped up as well as AMD. The problem really becomes that for SME’s that are getting into AI work, they’re going to find it much easier to use Nvidia because Nvidia has already done the “difficult” part of the job because everything they offer is specifically designed around DGX. As a result, those SME’s trying to jump off the ecosystem later will find it much harder. They are also pulling an Adobe and offering DGX to the educational sector to try and get new graduates hooked to the ecosystem that Nvidia provides.

Kinu4U

Tsla is using DGX to power their driving

_Lucille_

2 things excited me: 1: Alphamayo. This is potentially pretty big: now a car manufacturer can just take this and give a shot at AV. I am going to need to see the new Mercedes Benz in action: it has no giant bulky sensor like a Waymo (though prob still has lidar), and it being production ready (to a point where you can buy one soon in the US) may be a pretty big disruptor in the robo-taxi game. One thing of note is that their partners are a giant list of Chinese EV makers. While companies like Stellantis, Uber, Benz, and Lucid are there, likely it is the BYDs and Xiaomis that will make this super main stream, esp if they are allowed to be tested in Chinese cities. 2) The whole DGX platform: like, how is anyone supposed to even compete with that? Feels like nvidia has taken the whole data center game to the next level. At the end of the day, even with a bubble burst, AI will still be all around us and utilized, and nvidia has given a strong reason for data centers to keep buying their stuff. (I jokingly made a remark of when he did the blackwell comparison: okay, so peak performance will be 10x, will the price of one of these also be 10x?)

3xshortURmom

The personal ai agent robot called Reachy Mini that they want to provide for every desk is a very interesting product. [NVIDIA brings agents to life with DGX Spark and Reachy Mini](https://huggingface.co/blog/nvidia-reachy-mini)

TinFoilHat_69

Why a 5090 sell for 5k when an rtx 6000 can be had for 5-8k? Maybe 5090 TI super but it would need at least 128gb of vram if it’s going to cost more than DGX spark…….

sailortyx56

1x 256GB RAM Stick: $4,799.99 Nvidia DGX Spark: $3999.99 Mac Studio (M4 Max, 128GB unified memory): $3,329.99 lol

iceland00

The Nvidia’s-Groq deal is enormous. Nvidia now owns both the best training and inference platforms. By excluding GroqCloud, we clearly see NVDA stating that it will not be a cloud compute provider. DGX Cloud becomes strictly R&D, POC kind of platform for partners and internal use. Nothing against Google. NVDA is absolutely killing it though.

dont_downvote_SPECIL

Nvidia has shifted to using low-power DDR in its DGX GB300 to save on server electricity budgets. This puts memory demand from Nvidia servers into direct competition with high-end smartphone makers, which use the same DDR chips. And it all comes amid a push to add more memory to premium consumer devices so that they can do on-device AI.

petrrrrrd

Nvidia’s AI moat in 2025 is still enormous, but the gap is finally narrowing at the edges. The core of the moat is not just GPUs; it’s the full‑stack ecosystem and lock‑in around CUDA plus Nvidia’s scale in data‑center AI. Why the moat is so deep • Nvidia still controls the large majority of AI accelerators in data centers, which gives them pricing power, massive R&D budget, and deep integration with every major cloud. • CUDA has effectively become the default “OS” for accelerated compute. Millions of devs, tons of tooling, endless tutorials and pretrained models are built assuming Nvidia GPUs. • They’re now full‑stack: GPUs, networking (Infiniband, NVLink), systems (DGX/HGX), and increasingly software platforms (Nvidia AI Enterprise, libraries, SDKs). Ripping this out is expensive and risky for big customers.

grassmunkie

Difference this time is it is a structural shift in demand for new DRAM products that are based on long term contracts, and for the most part inelastic to price hikes. All the fabs are shifting to HBM and SOCAMM2, which is 5 times more expensive. The B100 gpu has 192GB of HBM, the newer ones are 288GB. A medium sized deployment is 128 servers of DGX B200, which is 2.3TB of HBM each. Do the math. These numbers are insane. DRAM is now the bottleneck for AI datacenter build outs so Nvidia, Google, Meta, Apple, pretty much everyone, is forced to contract out for years to secure the supply. For OpenAI and Nvidia, this is also about taking all the chips away so competition can’t grow. The war is being waged via DRAM, the winners are the Memory oligarchs, Micron, Samsung, SK Hynix.

crazier_ed

Just a small note the 200k is for the dgx... The GPU was 10- 15k which probably boils down to like 50c an hour once u add some server cost? From Google search: AI Overview NVIDIA A100 PCIe 80 GB Specs | TechPowerUp GPU Database When NVIDIA launched the A100 in May 2020, it was positioned as a high-end AI accelerator, with systems like the DGX A100 costing around $200,000, while individual GPU modules (40GB/80GB) had street prices starting from roughly $10,000-$15,000, depending on the reseller and variant (PCIe vs. SXM),

3xshortURmom

NVIDIA’s Rubin platform is set to launch in 2026 and will redefine ai computing by enabling million-token context windows, generative video, and agentic ai which will further cement NVIDIA’s leadership in training and inference while expanding its reach into next-gen applications. Rubin will reshape the ai landscape with a massive leap in ai performance. Rubin context processing extension (CPX) is built for massive context inference and enables models to process million-token sequences which is game changer for code generation (such as full software systems), generative video, and agentic ai (utilizing multi-step reasoning and planning). The NVL144 CPX platform delivers 8 exaflops of ai performance and 100TB of fast memory per rack dwarfing current Blackwell capabilities. Nvidia’s annual chip cadence means relentless innovation. Rubin is part of NVIDIA’s new annual release cycle, with Rubin Ultra slated for 2027 and Feynman in 2028. This cadence pressures competitors (like AMD, Google, and Amazon) to match NVIDIA’s pace in both hardware and software evolution. Will they keep up? No. They won’t. Nvidia is expanding beyond data centers. Rubin is designed not just for hyperscaler training, but also for sovereign ai infrastructure (national scale deployments), enterprise ai factories (like custom LLMs and vertical ai) and Nvidia is working on edge inference at scale (via modular Rubin variants). The economic impact of this is a trillion dollar ai boom. J Huang projects that agentic ai will require 100x more compute than previously forecasted. Rubin’s efficiency and scale could unlock $5B in token revenue per $100M invested in infrastructure. Nvidia has a strategic moat based on software and systems. Rubin is tightly integrated with CUDA and TensorRT for developer lock-in as well as NeMo and DGX Cloud for enterprise ai deployment with NVLink and InfiniBand for ultra-fast interconnects. Nvidia’s full-stack approach an unmatched platform with multiple next gen chips in the works. The conclusion that matters is that Rubin will extend NVIDIA’s already superior lead. Don’t let the hype and news shake you (or deter you) from the clear winner. While Google and Amazon are gaining ground in specific inference uses, Rubin will reinforce NVIDIA’s dominance in training frontier models, high-context inference and ai infrastructure at national and enterprise scale. Let’s also not forget who is destined to get the lion’s share of international contracts also.

3xshortURmom

Regarding Google and Amazon Custom AI Chips TPUs (v5e, v6) are optimized for training and inference and are tightly integrated with Google Cloud. Trainium3 (training) and Inferentia2 (inference) offer high performance at lower cost and energy. TPUs are native to Google Cloud’s Vertex AI, enabling seamless scaling for LLMs. AWS is the largest cloud provider. Trainium3 is embedded in UltraServer systems for enterprise AI. TPUs are often cheaper and more power-efficient than general-purpose GPUs. Trainium3 uses 40% less energy and delivers 4x the performance of its predecessor. But Google designs chips for its own AI workloads (like Search, Bard, YouTube). And Amazon uses its chips to power Alexa, AWS services, and internal LLMs. With that said, NVIDIA still controls 80–90% of the AI training chip market, but custom ASICs are growing faster. There obviously is some hyperscaler defection risk. Meta, Google, Amazon, and Microsoft are all designing in-house chips to reduce reliance on NVIDIA. Meta is testing Google’s TPUs for future workloads. But they can’t replace their reliance on Nvidia anytime soon. NVIDIA’s GPUs can sometimes be considered overkill for some specific inference tasks. Meaning they are more powerful than a specific task may require. Amazon’s Inferentia2 and Google’s TPUs can offer cheaper, more efficient alternatives for some very specific production-scale inference. As hyperscalers shift to in-house chips, NVIDIA may face pricing pressure and reduced volume in its highest-margin segment. Yet NVIDIA Still Leads. CUDA Ecosystem Lock-in makes it difficult to switch. Developers are deeply entrenched in NVIDIA’s software stack, making switching costly. Nvidia has substantial performance leadership. Blackwell GPUs remain the gold standard for training frontier models. Nvidia is also working in the next gen Rubin line that will be released in 2026 which will make a clear statement of continued dominance. Nvidia has full-stack AI Infrastructure. NVIDIA offers not just chips, but networking (NVLink, InfiniBand), systems (DGX), and software (TensorRT, NeMo). So, outlook? Some fragmentation, but not outright replacement Hyperscalers would love to produce everything NVDA does in-house and maintain quality standards but they simply can’t and therefore won’t replace NVIDIA, but they will carve out share in specific domains (like inference, internal workloads). NVIDIA’s biggest risk is losing hyperscaler loyalty, not because of inferior tech, but because of cost, control, and vertical integration. But these are problems that can be resolved. By 2028, NVIDIA is projected to lose some AI chip market share to custom ASICs from Google, Amazon, and others but it will remain the dominant player in training workloads. Regarding AI Chip Market Share Projections (thru 2028) NVIDIA will still have appx 80% (training), 60% (overall). Still dominant in training but loses some inference share to ASICs. Google (TPU) appx 5–7%. TPU production could reach 7M units by 2028. Amazon (Trainium/Inferentia) appx 3–5%. Gains in inference, especially within AWS. AMD appx 5% - 10% MI300X adoption grows, especially in cloud and HPC. Intel (Gaudi) <3%. Gains traction in cost-sensitive enterprise AI. Others (startups, China) appx 10%. Includes Hailo, Tenstorrent, Huawei Ascend, and domestic Chinese players. En resumen… by and far NVIDIA Still Leads CUDA ecosystem lock in creates a moat. Developers and enterprises are deeply embedded in NVIDIA’s software stack. Blackwell and its successors remain the gold standard for training frontier models. This is training dominance. Full-stack integration from chips to networking (NVLink, InfiniBand) to software (TensorRT, NeMo), NVIDIA offers unmatched vertical depth. Fin

wtf_is_up

Can't bear to hear Rogan tell the same story about wolves, aliens, and gorillas for the millionth time. Here's a summary of Big J's episode: >Executive Summary: The conversation between Joe Rogan and Jensen Huang centers on 4 main themes: the geopolitical and energy context of artificial intelligence, the nature and trajectory of AI capabilities and risks, the economics of compute and Nvidia’s strategic positioning, and Huang’s personal and corporate history as a case study in entrepreneurial risk, resilience, and culture. Huang characterizes AI as the latest phase of a long-running global technology race that confers “information, energy and military superpowers,” with national prosperity and security depending on energy growth, industrial capacity and technological leadership. He credits pro‑growth U.S. energy and onshoring policies in the previous administration with enabling the capital‑ and power‑intensive build‑out of AI factories and chip fabs, arguing that without such policies “we would not be able to build factories for AI.” >On AI trajectory and risk, Huang rejects a singular “event horizon” moment and anticipates a gradual, continuous improvement process, with multiple competing AIs balancing one another much like offensive and defensive cyber systems. He acknowledges serious concerns about military applications, cyber security and quantum‑era encryption but argues that the same AI technologies will be deployed at scale for defense, monitoring and post‑quantum cryptography. He views AI primarily as a new class of software whose growing power is being channeled toward safety, accuracy and controllability, noting that in the last 2 years AI capability has increased “maybe a 100x,” with much of the incremental compute redirected into reasoning, research, reflection and tool use that reduce hallucinations. >Economically, Huang expects AI to augment rather than universally displace labor, emphasizing the distinction between a profession’s purpose and its constituent tasks. He points to radiology, where deep learning has “swept the whole field” but radiologist headcount has increased because image reading was only a task in service of diagnosis. He anticipates new categories of work around robotics, AI operations and maintenance, and believes that in the next 5–10 years AI will materially reduce the technology divide because it is the easiest tool in history to use, will run locally on phones, and will provide “yesterday’s AI” at low cost to nearly all countries. He is more skeptical about clean universal basic income narratives, arguing that discussions of universal abundance and large‑scale public income support cannot both be true in their extreme forms. On compute economics, Huang explains Nvidia’s thesis that traditional Moore’s law improvements are no longer sufficient and that accelerated computing has become the dominant performance driver. Over the last decade Nvidia’s approach has delivered roughly 100,000x improvement in AI computing efficiency, which he compares to a car becoming 100,000x faster or 100,000x cheaper to operate. The DGX1 AI supercomputer he delivered to Elon Musk in 2016 delivered 1 petaflop of performance in a $300,000 rack‑scale system; 9 years later the DGX Spark he hands Musk at SpaceX offers a similar 1 petaflop in a device the size of a book costing about $4,000. He projects that AI will remain energy constrained and data centers will increasingly require dedicated generation, including “hundreds of megawatts” small nuclear reactors over the next 6–7 years, but argues that per‑task AI energy requirements for most users will eventually be “utterly minuscule.” >Huang’s recounting of Nvidia’s history highlights repeated near‑death experiences, highly concentrated strategic bets, and a culture organized around first‑principles reasoning, constant reassessment and an unusual tolerance for vulnerability from the CEO. Early Nvidia made three major technical choices for 3D graphics that were “all wrong,” nearly failed trying to build a console chip for Sega, and survived only because Sega’s CEO converted the last $5 m of a development contract into equity despite acknowledging it would “most likely be lost.” Later, Nvidia risked half its remaining cash on an emulator from a failing company and convinced TSMC’s founder to fabricate a new chip directly into volume production without the usual silicon test spin. These decisions led to the Riva 128 and subsequent GeForce lines that effectively collapsed million‑dollar image generators into a consumer graphics card and, ultimately, to the CUDA accelerated‑computing stack that underpins modern AI. >Huang describes himself as driven far more by fear of failure than by desire for success. He works 7 days a week, wakes around 4 a.m., reads “several thousand emails a day,” sleeps 6–7 hours, and says he has used the phrase “30 days from going out of business” for 33 years. He emphasizes that his leadership style deliberately presents vulnerability so that employees feel free to challenge his assumptions and to pivot the company’s strategy when needed. The episode closes with Huang’s immigrant background—sent alone with his brother from Thailand to a harsh Baptist boarding school in rural Kentucky, reunited with parents who arrived with almost no money, and ultimately becoming what he calls “the first generation of the American dream”—providing a narrative frame for Nvidia’s corporate trajectory and his current stance that the United States remains uniquely capable of creating such opportunities.

greenpride32

NVDA shipped their very first DGX 10 years ago - it just shows how far ahead NVDA is or how far behind AMD is.

iMakeGOODinvestmemts

Seaport Global Securities on $NVDA (Sell, PT $140): "We see Nvidia facing growing competitive pressure." "To address this, the company has been leaning on a variety of sales mechanisms to adapt. These measures are not fully reflected in financials, but they are already material and look likely to grow significantly next year. We remain negative on Nvidia as signs of competition increase: Nvidia has $26 billion of cloud compute service agreements." "The company maintains that these will be used for R&D and its DGX offering. We see these as a form of rebate which, if recognized, would take 400bps off gross margins next year, or at least $0.30. Google has surprised with its ability to promote third party use of its internally designed TPUs." "TPUs are not for everyone, but can outperform Nvidia systems on many metrics. Growing commitments and investments to customers. The company spent $6 billion this year in private companies. It has commitments for another $17 billion (including $5 billion to Intel)."

EpicOfBrave

**You’re wrong again**. NVIDIA is involved in building QPU. Start with NVIDIA DGX Quantum: https://www.quantum-machines.co/products/nvidia-dgx-quantum/ They also develop with 15+ QPU vendors, because you don’t optimize CUDA-Q and all other components for a device without being part of its development. Please learn how hardware–software co-design actually works. Your claim of “interacting with QPU just by building GPU” is exorbitantly ignorant. Maybe try not to use world’s worst search engine.

EpicOfBrave

**You’re wrong again**. NVIDIA is involved in building QPU. Start with NVIDIA DGX Quantum: https://www.quantum-machines.co/products/nvidia-dgx-quantum/ They also develop with 15+ QPU vendors, because you don’t optimize CUDA-Q and all other components for a device without being part of its development. Please learn how hardware–software co-design actually works. Your claim of “interacting with QPU just by building GPU” is exorbitantly ignorant.

EpicOfBrave

You’re wrong again. **NVIDIA is involved in building QPU**. Start with NVIDIA DGX Quantum: https://www.quantum-machines.co/products/nvidia-dgx-quantum/ They also develop with 15+ QPU vendors, because you don’t optimize CUDA-Q and all other components for a device without being part of its development. Please learn how hardware–software co-design actually works. Your claim of “interacting with QPU just by building GPU” is exorbitantly ignorant.

SuperSultan

You can rent a DGX A100 through a public cloud provider and pay hourly which is way more cost effective. Is there an issue with this?

XYHopGuy

98% of posts in stock subs about this are circular and ignore public evidence, earning call transcripts, and financial statements. While also having zero context for ML and semis. It's not hard, they should try to rent a DGX A100 node and see how it goes.

Beginning_Basis9799

Some quick web research on server GPUs that are 5 years old. This does not look positive on depreciation. So 5 years old in server graphics cards would be ampere generation. As of today we are on Blackwell next year we are on Rubin. DGX a100 early Jan 2020 would have 40gb vram. Now let's jump to recent server gpu with vram b200 192gb. That's 4* speed increase nearly. If a competitor uses a newer GPU which at base has increased at a jump of 4* the amount. They are 4* faster than you in compute. But maybe it's worth something, so not much better news here. Release price DGX A100 = $199,000 Price today Second hand cost now aroud £26,000 uk so $37,000 Please don't take my word research this yourselves.

Fkn1v1mem8

NVIDIA isn’t winning just because their GPUs are fast — Google’s TPUs are actually monsters at the specific math they’re built for. The problem is that TPUs are basically a super-fast screwdriver, while NVIDIA GPUs are a Swiss Army knife with a power drill strapped to it. Hyperscalers want hardware that can run every model, not just the ones TPUs love. NVIDIA has CUDA, cuDNN, TensorRT, thousands of libraries, a massive dev community, and people already trained to use it. TPUs? Great at tensor ops, but way more niche, way harder to integrate, and you can’t even buy the good ones because Google keeps the top versions for itself. On top of that, NVIDIA’s whole ecosystem (NVLink, NVSwitch, DGX racks, etc.) scales ridiculously well across huge datacenters. TPUs can scale too, but only inside Google’s walls. Nobody else wants to depend on Google — a direct competitor — for their core AI hardware. So even though TPUs can be faster, NVIDIA wins because they’re flexible, mature, everywhere, and come with the software glue that actually makes massive AI training work. In short: TPUs are specialized rockets; NVIDIA is the entire airport, fuel system, pilot training program, and air-traffic control.

jobrody

I'm not sure the figures quoted in that story are apples with apples. The story says a B200 costs $500k, but rents for under $3.20/hour. According to Gemini (no, I haven't done deeper digging), the $500k price tag is for a DGX B200 server, which includes 8 individual chips. That server then rents out for $45-$60/hour. It's still a steep differential, but the ROI decimal needs to move over one space.

moorhound

Buying a NVDA DGX H100 SuperPOD: $18-20m, plus you've gotta pay for power, cooling, the site, and other upkeep Renting equivalent cloud compute from AWS, Azure: $10-12m per year Renting equivalent compute from a Google TPU v5p Pod: $8.5m per year If you can find cheaper compute, it's probably booked out til 2030. Google will win the AI war.

Nice_Cellist_7595

AMD may also play a role in this. Their new repurposed GPU on die accelerators may be sufficient for inference at some scale. Wildly cheaper - but not suited to training, it will bifurcate the AI market into specialized hardware products for for Training and Inference at scale where for now there is only NVDA. The major limitation for widespread training competition that I see after looking into all of this is that NVDA is still the king of memory bandwidth. Those sweet sweet 74% margins are going to go the way of the dodo. It was always going to go this way and there is a lot between the hardware and a usable ecosystem for AI such as good drivers that are very stable. Further, xAi and Tesla's custom silicon is probably going to end up being a blow to demand for NVDA as well. And .. totally anecdotally, I went looking for a high end GPU recently and not only did I have a choice, I also paid MSRP. This hasn't been the case for \*years\* between crypto and AI. I know they said something about the GPU market being robust and blackwell being supply constrained. This may be the case for the DC hardware but it isn't for the consumer and pro cards cards. (Which then makes me wonder what part of the DC hardware is constrained) The DGX also shipped relatively on time and I'd set way met with lack luster demand. Don't get me wrong it looks cool but it's also pretty slow for what it costs.

ahfoo

Meta is rumored to be reporting failing GPUs in their DGX200 units with six GPUs that cost a half million a pop and require specialized power power circuits to operate and therefore have little to no aftermarket resale value. The exact rate of failure is unknown but reportedly about 10% annually for the GPUs mostly from overheating. Those chips begin losing their value as soon as they leave the fab. Also, in all of their products from gaming GPUs to crypto to "AI", Nvidia uses a most likely illegal technique called "signed drivers" which mean the customer never really owns the equipment, the hardware is merely a token of a software license that Nvidia retains control of through the drivers which are licensed, not sold. This massively inflates depreciation in the same way that people don't want old Tesla autos because they don't trust the parent company to play fair.

moorhound

NVDA will always have a spot somewhere, their stuff is still the king of raw model training power. Once companies start getting the models trained though, they're gonna put the compute focus on inference, and that's where TPUs shine. Mass AI rollout is going to eventually boil down to cost efficiency, and the jack-of-all-trades DGX systems are *hella expensive*. TPUs will do the job for cheaper, and with less power draw, which is what you need for on-device AI for stuff like self-driving AI cars and robots.

reddit_lemming

You use the shiny new stuff for training models, and the slightly used shit for inference. Or image processing. Or whatever else where running on some sort of GPU is preferable to CPU-only. In our case it’s ML inference and image processing. Some of our researchers are working on H100s/H200s, but we’re still getting great mileage out of our older A100s. Hell, one of our guys is still running a DGX with fucking VOLTAS. Works well enough for him.

Fearless_Strike5651

Curious what AI are you working with that’s messing up like that? Because the ones running on DGX clusters, Azure AI, aren’t exactly known for wrong answers. Especially the wrong date lol the early versions didn’t even do this . Those systems are literally optimizing logistics, defense, and finance in real time. I doubt it’s the same setup?

SuspiciousSpyderman

If you want something thats relatively safe and good fundamentals, then DGX. Or you could gamble on random biotech companies

moorhound

After Elon's shareholder meeting, I expect CEO bullshitting to up their game. Jensen: "We are penning a deal with Blockbuster in which we will invest $1B in DVD rewinders and they will buy $5B in NVIDIA DGX platforms" Zuck: We anticipate 40% of Americans will trade their prescription glasses for Meta Quest 4 headsets by 2028" Karp: "Our short sellers will fail. You know what, fuck them. Here's their home addresses" Altman: "AI is going to be everywhere. It already is. Look to your left. See your wife? She's an AI"

Gross-Salary

For context: A 'Kkanbu Alliance' of AI between leading companies from Korea and the United States was formed at a Korean chicken restaurant. Jensen Huang, CEO of NVIDIA, Lee Jae-yong, Chairman of Samsung Electronics, and Chung Eui-sun, Chairman of Hyundai Motor Group, met on the 30th at a chicken restaurant in Gangnam-gu, Seoul, for a three-way 'chimaek' (chicken + beer) gathering. The meeting lasted for about three hours, including the NVIDIA event held nearby on the same day. ● Unprecedented Meeting of Corporate Leaders The meeting was unprecedented. CEO Huang remarked, "Today is the best day of my life." The leaders of global companies, including NVIDIA, Samsung Electronics, and Hyundai-Kia Motors, with a combined market capitalization of approximately KRW 8,300 trillion, visited the 'Kkanbu Chicken' store near Samseong Station in Gangnam-gu, Seoul, and enjoyed a public chimaek in front of hundreds of citizens. CEO Huang entered the chicken restaurant with Chairman Chung around 7:20 PM after arriving in Korea. He wore his signature black leather jacket and a black T-shirt. Chairman Lee arrived about five minutes later and embraced CEO Huang. Chairman Lee and Chairman Chung also wore casual white T-shirts. This gathering was arranged because CEO Huang wanted to experience Korea's chimaek culture. CEO Huang ordered fried chicken, spicy sea snails, and cheese sticks to share with Chairman Lee and Chairman Chung. They drank beer and also tried 'soju tower,' a device for mixing soju and beer, consuming several glasses. The three exchanged drinks in a 'love shot' style. When CEO Huang exclaimed, "Dinner is Free," Chairman Chung replied, "I'll cover the second round." However, it is reported that Chairman Lee actually paid the bill. The total meal cost at the restaurant was approximately KRW 2.5 million. ● Kkanbu Alliance Continued into the Night Jensen Huang, CEO of NVIDIA, takes a commemorative photo with Lee Jae-yong, Chairman of Samsung Electronics, and Chung Eui-sun, Chairman of Hyundai Motor Group, after a 'chimaek' gathering at Kkanbu Chicken in Gangnam-gu, Seoul, on the 30th. The informal demeanor of the global corporate leaders was broadcast live during the meeting. CEO Huang left his seat to distribute kimbap, banana milk, and chicken to citizens. During this time, Chairman Lee remarked, "It's been about ten years since I last had chimaek," to which Chairman Chung replied, "I eat it often." CEO Huang gifted Chairman Lee and Chairman Chung a bottle of Japanese Hakushu 25-year whiskey worth approximately KRW 7 million and NVIDIA's 'DGX Spark' AI supercomputer. The gifts were signed with the message, "TO OUR PARTNERSHIP AND FUTURE OF THE WORLD!" Lee Jae-yong, Chairman of Samsung Electronics, distributes chicken to citizens during a 'chimaek' gathering with Jensen Huang, CEO of NVIDIA, at Kkanbu Chicken in Gangnam-gu, Seoul, on the 30th. The choice of venue, 'Kkanbu,' which means close friend, was interpreted as a nod to the famous line "We are kkanbu" from the Netflix drama 'Squid Game.' CEO Huang stated, "I enjoy chimaek with friends, so Kkanbu is the perfect place." He repeatedly expressed, "So good. So Happy," at the chicken restaurant. Chairman Lee, leaving the restaurant, commented, "Happiness is nothing special. It's about enjoying good food and drinks with good people." The late-night chimaek gathering, lasting about an hour and twenty minutes until 8:40 PM, continued at the 'GeForce Gamer Festival' hosted by NVIDIA at COEX in Gangnam-gu, Seoul.

Indiusa

I just bought a DGX from NVDA… nah if they were that sold out why even bother send me a desktop…

SuspiciousSpyderman

Bought the dip on MSFT and continuing to hold DGX and AZO

SuspiciousSpyderman

I need DGX and AZO to start moving up

981flacht6

50% of Apple's sales are iPhones and they're a $4T company. Nvidia is doing something exponentially greater for humanity. I've been on this roller coaster for 7 years, I've heard every bear argument since then. The reason I bought Nvidia was bc of the DGX, and I thought..wow if they can solve autonomous driving then they will be worth a lot of money. I didn't think they'd have gotten here at that time. There's a lot more to come.

SuspiciousSpyderman

I'm gonna need DGX and AZO to pump

SuspiciousSpyderman

DGX, AZO, no bias

MetricT

I work in HPC. Why would I spend thousands of my own money on too much hardware, when we have racks of DGX hardware? I wasn't born with money. I earned it, by working hard, saving hard, doing without. And by *not* spending it when stocks are stupidly overvalued. Buffett isn't either.

SuspiciousSpyderman

Autozone (AZO) and Quest Diagnostics (DGX), hoping to buy low after they have taken a beating last week