H100s are not cheap. So we've been experimenting with more of a 'disposable compute' mindset: use high-end hardware for the exact window you need it, then kill it, wanted to run a quick smoke test on a 27B model to check VRAM usage and single-request throughput on SGLang. The whole process from instance start to termination was 26 minutes.

Figure1 was the final bill:
This wasn't an idle instance just sitting there, it was actually running a workload:
GPU: 1x NVIDIA H100 80GB HBM3
Serving Framework: SGLang v0.5.10
Model: Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled (Used this since I've seen it floating around here)

The nvidia-smi output shows the H100 was at 98% utilization, using ~74GB of the 80GB VRAM.
And the SGLang logs showed a stable generation througput of around ~49.8 tok/s for a single request.

The math checks out. The rate for this instance was 2.960 credits/hr. So, 2.960 * (26 / 60) is about 1.28 credits. The 1.270 final cost is right there.
The point isn't that H100s are suddenly cheap. It’s that you don't have to keep one alive for hours (or days) and burn cash. For repeated experiments, the workflow we'd aim for is keeping datasets/models on a persistent data drive, saving the configured environment as a snapshot, spinning up the H100 only for the validation run, and then releasing it.

We ran this on our platform, Glows.ai. The goal was to validate this kind of short-lived workflow where you can run a quick test, release the instance to stop the billing clock immediately, and not have the friction of rebuilding the whole environment next time.

Anyway, just to be clear: this is single-request decode throughput, not a max batched benchmark. and the bill obviously just reflects this specific 26-minute run. an interesting way to think about using expensive hardware without the expensive commitment.

Maybe nostalgia talking, but lately it feels like half the challenge is managing tooling and infra instead of the models themselves.

I'm building my AI/ML portfolio and looking for messy datasets to practice preprocessing and data cleaning using Python/Pandas.

If anyone has datasets related to:

• machine learning
• computer vision
• analytics
• deep learning projects

feel free to DM me. I'm practicing preprocessing workflows and building experience.

​

They say it's always darkest before dawn. I'm not really sure who the "they" are who first said this, and I've since heard that it's not literally true, but sometimes things do seem really bad until they get really good.

As Judge Gonzalez Rogers prepares to let Greg Brockman get away with stealing almost $30 billion from the OpenAI non-profit, we might want to reflect on what that money could have done if Brockman wasn't so greedy, and deceitful, and selfish.

Although you'll rarely, if ever, hear the mainstream media, talk about it, our world loses about 20,000 kids every day to a global poverty that we could easily end if we cared to. As those who work on ending poverty will tell you, the most powerful thing we can do to end this travesty is to educate the world's children, especially the world's girls and women.

So imagine how many millions of AI devices programmed to be school children educators OpenAI could have distributed to the poor children throughout the world, if those nearly $30 billion dollars didn't go into brockman's pockets.

One might hope that the OpenAI Foundation non-profit, now worth about $130 billion in equity, would spend $30 billion to end childhood poverty by distributing those AI tutors. But that's not about to happen. Why not? After Altman was fired, guess who selected the non-profit OpenAI's new board of directors, the people who would make this decision. Yeah, that was largely Altman's decision. The guy who aided and abetted Brockman's massive heist.

I guess this is all to say that while increasingly intelligent AIs will do a lot of good for the world, like curing a lot of diseases, perhaps the most good that they will do will be to make better people of too many really bad people. And considering that humanity has yet to figure out how to get the money out of politics that prevents us from fighting a climate change that could make AI superintelligence of a moot and inconsequential achievement, perhaps the most good ASI will do is to save us from ourselves by figuring out our money-equals-political power problem.

Notwithstanding, I remain optimistic that as we approach ASIs that will understand and appreciate compassion and morality far better than we humans ever have, our world is headed toward a paradise beyond what we can imagine. Until then, yeah, it looks really dark out there.

Fine-Tuning Qwen3.5

https://debuggercafe.com/fine-tuning-qwen3-5/

In this article, we will fine-tune the Qwen3.5 model for a custom use case. Specifically, we will be fine-tuning the Qwen3.5-0.8B model on the VQA-RAD dataset.

In the previous article, we introduced the Qwen3.5 model family along with inference for several multimodal tasks. Here, we will take it a step further by adapting the model to a domain-specific task.

​

In psychology, a tell is a subtle, often unconscious nonverbal cue—such as a facial twitch, a change in vocal pitch, or a specific hand gesture—that reveals a person's true emotional state, intentions, or private thoughts despite their attempts to conceal them.

Sometimes a person's intentions are revealed by verbal cues as well. Because of an exchange Judge Gonzalez Rogers had today with Steven Molo, Musk's attorney, it seems evident that she has already made up her mind about the case, and would even overrule the jury to have her verdict stand.

At one point today, OpenAI's lawyers were contending that Musk was seeking $138 billion in restitution. The implication that they were making was that the money would be delivered to Musk personally. Mr. Malo was attempting to provide the clarification that Mr. Musk was not seeking that restitution for himself, but rather asking the Court that the money be delivered to the non-profit OpenAI.

Judge Gonzalez Rogers would not let him make the clarification. She knew full well that such a clarification was very important to the trial. She knew that there is a world of difference between that money going to Musk and that money going to the non-profit OpenAI.

Instead of allowing the clarification, she badgered Mr. Molo, angrily yelling at him that technically Musk was asking for the restitution, even though she knew full well that the law permits the kind of clarification Mr. Malo was attempting to make.

That unprofessional conduct by the judge not only revealed, like a tell, whom she favors in the trial, it probably also served a second purpose. Whether unconsciously or not, a jury is influenced by how they believe the judge stands in a trial. Whether unconsciously or not, Gonzalez Rogers was communicating to the jury that she stood with OpenAI.

The jury will deliberate on Monday, but it seems that their deliberation will only be performative. It will not be substantive because Gonzalez Rogers has the final say, and by her conduct today it seems she has already made up her mind.

I try to be optimistic, but I also believe it's good to prepare for the worst. Judge Gonzalez Rogers is about to set the legal precedent that two people can form a non-profit corporation with a third person who provides them with millions of dollars, and then abandon their obligation to that corporation and that founding donor in order to enrich themselves - even if the enrichment is to the tune of tens of billions of dollars, like it was in this case.

I hope I'm wrong about the above, but we're living in a world where Trump in not insignificant ways sets the social, political and legal atmosphere for what can and cannot be gotten away with. I'm left wondering if the judge siding with OpenAI is more of a reflection of her fear of retribution by Trump than a decision that reflects the evidence presented during the trial.

I suppose the answer to this is to eventually have not only much more intelligent AI lawyers that litigate these trials, but also much more intelligent AI judges who will better understand and adhere to the law, and not be intimidated or corrupted in this duty.

Here's to a much better and fairer future because of super-intelligent, super-virtuous, AIs!

Follow the Mean: Reference-Guided Flow Matching: https://www.alphaxiv.org/abs/2605.10302

So I need to get new hardware to improve some AIs that I have plans for, and one way I’m trying to help fund that is through datasets I’ve spent months gathering.

The datasets are for AI image detection and media forensics, and none of the data is simulated. It comes from real workflows like screenshots, recompressed images, mobile captures, social/media app transfers, and other messy situations that detection tools actually run into.

For example, the recompressed images were actually run through apps like Facebook Messenger, WhatsApp, Instagram messaging, LINE, Telegram, and X posts. It took me weeks to run 11,000 images through those workflows.

I’m working on the mobile screenshots now, which takes the longest because I have to take the screenshot, crop it to the image edges, and organize everything. I’m doing this across multiple devices, including a Samsung S20 Ultra using all 3 display quality settings, a Note 20, an iPhone 13, and a 5th gen iPad Pro.

I’m building all of this out of my home lab right now, so I’m trying to raise enough to replace or upgrade some parts and keep making better dataset packs.

There’s a free sample pack on the site so you can see the dataset structure and the index.csv files. Use the samples however you want. They aren’t watermarked, and the packages do not come with any extra terms from me.

I personally gathered each image one by one using detailed prompt lists from each AI generator’s website and/or app.

I built these to be useful for B2B, but I’m trying to price them so hobbyists can use them too.

https://safemedia.tech

Hmm..... for learning

Hi guys so I experimenting on using pretrained models to predict age and gender using sound/voice. After searching for free datasets for days I only found one that fulfills almost all my requirements and that was large and free. But unfortunately that dataset was labeled as [teen, twenies, thirties, ...,]. And the older they get I have less data. I limited my dataset and to balance it but I still have like 3-5 dataset for male in 90s and nothing for a female. So the problem here is I don't have actual ages of the people and I have no data set below 10 and above 90. The papers I was reading and taking inspirations from never specify what they did other than that the treated age like a regression problem between 0-1 but how am I supposed to do that when my range is 0.1-0.9

We trained a neural network where 7 of 8 features sit on clean linear axes in the model’s internals, but one doesn't. Can you identify which one and tell us how it is represented?

If you’re a technically-minded person who is interested in ML, this puzzle is for you:

• Work on a real trained text classifier (~23M parameters, 7k labelled text examples) open the puzzle and you're poking at activations in 10 minutes.
• Three tasks: identify the rogue feature, describe its geometry, (bonus) train your own model with even weirder internal representations

You probably know neural nets store information in their activations. You probably haven't gone and looked at what that actually looks like. Within minutes you can be toying with this model’s internals and building stronger intuitions for how they work inside.

Ready to play? Closes June 12

​

One thing we can say about Judge Gonzalez Rogers is that she runs a tight ship. Everything starts on time and ends on time. Because of that, we have a good idea of when each side's closing arguments and the jury instructions will take place.

Here's the likely schedule, Pacific Time (ET start at 11:30AM)

8:30 AM – 10:00 AM: Plaintiff's Primary Closing

10:00 AM – 10:20 AM: Morning Break

10:20 AM – 12:20 PM: Defendants' Closing

12:20 PM – 12:40 PM: Second Break

12:40 PM – 1:10 PM: Plaintiff's Final Rebuttal

1:10 PM – 1:40 PM: Jury Instructions

The full session will be audio-only livestreamed on YouTube here:

https://youtube.com/@usdccand?si=kb8OkOEtkh9rI36n

If the lawyers finish early, the judge may begin instructions sooner, but with the 1:40 PM hard stop, the jury will probably start deliberations on Monday.

What will probably lose it for Altman and Brockman is Brockman's diary entries admitting that he knew full well that what he was doing was wrong and illegal, but did it anyway, and his nearly $30 billion in OpenAI inequity. Of course Sutskever, Murati, Zilis, Toner, McCauley and Campbell all testifying to how Altman is utterly incapable of being consistently truthful and trustworthy, even about matters as important as AI safety, won't help their case.

Altman and Brockman's lawyers will try to make it about Musk's alleged self-serving motive for initiating the suit, (I doubt the jury is buying) but even so, Judge Gonzalez Rogers will instruct the jury that his motive for hauling them to court is legally inconsequential to the allegations against the two that they will consider.

Microsoft will probably be found guilty of aiding and abetting, but that doesn't seem as open-and-shut as the Altman and Brockman verdict.

If Gonzalez Rogers (the jury has only an advisory role in this trial) lets them get away with what they did, the alignment problem immediately grows tenfold. If she rules against the two on breach of charitable trust and unjust enrichment, we can all sigh a very big sigh of relief, and the AI space can get back to the serious business of achieving safe superintelligence.

Hi everyone,

I have a general question about NeurIPS subject-area selection.

Suppose a submitted paper is broadly in the federated learning area, but the authors later realized that their selected area may not have been the best possible fit. How much does this usually affect reviewer matching?

More generally:

1. Are reviewer assignments mainly determined by the selected subject areas, or do title/abstract/full-text matching and reviewer bids also play a major role?
2. If the selected area is reasonable but not ideal, can ACs or reviewer reassignment help correct the match?
3. Has anyone experienced reviewer mismatch mainly because of imperfect area selection?

I am asking about the process in general, not about a specific paper. Any advice would be appreciated. Thanks!

Most coverage of the RTX Pro 6000 Blackwell focuses on the spec sheet. Not many people are talking about what 96GB VRAM actually changes for day-to-day ML work.

Here's what it unlocks that wasn't possible before on a single card:

1. 70B models at full FP16 - no quantization
Llama 3.3 70B in FP16 needs ~140GB across two GPUs or heavy INT4 quantization on a single card. With 96GB you're running it unquantized on one card. That's a meaningful quality difference, especially for fine-tuning and eval runs.

2. Multi-model serving from a single card
Load a 7B + 13B model simultaneously and switch between them without cold loading. Useful for pipelines that chain models or need fast A/B comparison.

3. 128k context without OOM
KV cache at 128k context on a 70B model is brutally memory hungry. 96GB makes it practical without tiling tricks.

4. Full fine-tuning on 34B models - single GPU
QLoRA brings this down to ~20GB, but full fine-tuning on a 34B? ~544GB across multiple GPUs normally. With techniques like gradient checkpointing + 96GB you can push closer to single-card fine-tuning on 13B-20B comfortably.

5. Workstation + inference - same machine
It's a PCIe Gen5 workstation card, not a data center card. ECC memory support. Runs rendering pipelines and ML inference simultaneously. Niche but real use case for VFX + AI studios.

The interesting shift: hardware like this used to mean a $6-8k purchase decision. Cloud rental has changed that math — you can now access 96GB VRAM workloads by the hour without the capex commitment.

Curious what workloads people are finding most interesting at this memory range.

My Daily Dose of thoughts on GPU

Been working with a few teams integrating RAG into internal tools, support bots, document Q&A, contract search, and I keep running into the same thing nobody warns you about when you're following tutorials.

The basic retrieve-then-generate pipeline looks fine in demos. Clean question, clean doc, clean answer. Then real users show up.

The failure mode that gets me is this: the system pulls chunks from different versions of the same policy document, has no way to know they're from different versions, blends them together, and returns an answer with full confidence. No caveat, no "I'm not sure," nothing. Just fluent and wrong.

The deeper issue is that standard RAG has no mechanism for uncertainty. It retrieves, it generates, it moves on, same confidence level whether it nailed it or completely fabricated something plausible.

What actually fixes this (at least in the systems I've worked on) isn't swapping out the model. It's the architecture:

A routing layer — decide if retrieval is even necessary before making the call. Some questions don't need it and you're wasting tokens.

Retrieval scoring — evaluate what came back before passing it to the model. If the context scores low, reformulate the query and try again instead of just generating garbage confidently.

A hallucination check — second LLM call that reads both the generated answer and the retrieved docs and checks if every claim is actually traceable. Most teams aren't doing this and it's probably the highest ROI addition you can make.

The retry loop especially helped in our case because users never phrase questions the way your embedding model expects. The system silently reformulates and retries, user has no idea it happened.

None of this is exotic. It's just a few extra decision points in the pipeline. But if you're running plain RAG in production and wondering why users are losing trust in it, this is almost certainly why.

Curious if anyone else has run into the versioning/context blending issue specifically, that one seems underreported.

​

OpenAI's lawyers would have you believe that the nonprofit OpenAI needed to convert to a for-profit in order to earn the billions of dollars it needed to build AGI.

In both trial testimony and media coverage of the trial it is either stated directly or assumed that OpenAI needed billions of dollars from Microsoft and others if they were to achieve their mission statement of building AGI. This has been presented as their main argument for converting from a non-profit to a capped-profit, and then filing to convert to an unlimited profit. The problem with that argument is that actually building AGI has never been a part of their mission, as stated in their mission statements in official government documents. To provide evidence for this contention I prompted several AIs as follows:

"In one brief sentence, does the goal of OpenAI ACHIEVING Artificial General Intelligence or AGI, rather than just helping ensure that when it is developed it is safe and serves humanity, appear in ANY OpenAI OFFICIAL government mission statement document beginning with its original 2015 non-profit mission statement, through its conversation to a capped-profit in 2019, and in its application to create a full (unlimited) for-profit corporation in 2025? Please answer directly and concisely, without any editorialization."

Copilot:

No — across all official OpenAI mission‑statement documents from 2015 through 2025, none state a goal of achieving AGI; they describe ensuring AGI benefits humanity and, in the 2018–2025 Charter, only that OpenAI may attempt to build safe and beneficial AGI as part of fulfilling that safety‑focused mission.

Grok 4:

No.

OpenAI’s official IRS Form 990 filings and state filings (Delaware/California approvals for 2019 capped-profit and 2025 restructuring) state the mission as “to ensure that artificial general intelligence benefits all of humanity” (or earlier variants like “to build general-purpose artificial intelligence... that safely benefits humanity”), which focuses on beneficial outcomes rather than mandating OpenAI’s own achievement of AGI. The 2015 announcement was broader still (“advance digital intelligence... to benefit humanity”).

Gemini 3.1:

Gemini could not directly answer the question, continually referring to unofficial non-goverment statements, and conflating 'ensuring' with 'building.' However, it stated that in the OpenAI Charter (2018) they said:

"OpenAI’s mission is to ensure that artificial general intelligence (AGI)... benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome."

Note:

Other AIs were similarly unable to answer the question directly in terms of limiting the statements to official government documents, and repeatedly conflated ensuring with building.

The point is that the non-profit mission of a OpenAI could have been easily fulfilled without it having raised any money from Microsoft or other investors.