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You also need to consider that the field is moving really fast and you cannot really rely on being able to have the same margins in a year or two.

These are great points.

However, I don’t think these companies provision capacity for peak usage and let it idle during off peak. I think they provision it at something a bit above average, and aim at 100% utilization for the max number of hours in the day. When there is not enough capacity to meet demand they utilize various service degradation methods and/or load shedding.

Do we know how big the "batch processing" market is? I know the major providers offer 50%+ off for off-peak processing.

I assumed it was to slightly correct this problem and on the surface it seems like it'd be useful for big data places where process-eventually is enough, e.g. it could be a relatively big market. Is it?

If you are willing to spread your workload out over a few regions getting that many GPUs on demand can be doable. You can use something like compute classes on gcp to fallback to different machine types if you do hit stockouts. That doesn't make you impervious from stock outs, but makes it a lot more resilient.

You can also use duty cycle metrics to scale down your gpu workloads to get rid of some of the slack.

You're not wrong.

However, this all assumes realtime requirements. For batching, you can smooth over the demand curve, and you don't care about latency.

Re the overnight that's why some providers are offering there are batch tier jobs that are 50% off which return over up to 12 or 24 hours for non-interactive use cases.

> There are no on-demand GPUs at this scale.

> These two factors mean that GPU utilization comes in at 10-20%.

Why don't these two factors cancel out? Why wouldn't a company building a private GPU cluster for their own use, also sit a workload scheduler (e.g. Slurm) in front of it, enable credit accounting + usage-based-billing on it, and then let validated customer partners of theirs push batch jobs to their cluster — where each such job will receive huge spot resource allocations in what would otherwise be the cluster's low-duty point, to run to completion as quickly as possible?

Just a few such companies (and universities) deciding to rent their excess inference capacity out to local SMEs, would mean that there would then be "on-demand GPUs at this scale." (You'd have to go through a few meetings to get access to it, but no more than is required to e.g. get a mortgage on a house. Certainly nothing as bad as getting VC investment.)

This has always been precisely how the commercial market for HPC compute works: the validated customers of an HPC cluster sending off their flights of independent "wide but short" jobs, that get resource-packed + fair-scheduled between other clients' jobs into a 2D (nodes, time) matrix, with everything getting executed overnight, just a few wide jobs at a time.

So why don't we see a similar commercial "GPU HPC" market?

I can only assume that the companies building such clusters are either:

- investor-funded, and therefore not concerned with dedicating effort to invent ways to minimize the TCO of their GPUs, when they could instead put all their engineering+operational labor into grabbing market share

- bigcorps so big that they have contracts with one big overriding "customer" that can suck up 100% of their spare GPU-hours: their state's military / intelligence apparatus

...or, if not, then it must turn out that these clusters are being 100% utilized by their owners themselves — however unlikely that may seem.

Because if none of these statements are true, then there's just a proverbial $20 bill sitting on the ground here. (And the best kind of $20 bill, too, from a company's perspective: rent extraction.)

AWS is absolutely not cheap, and never has been. You want to look for the hetzner of the GPU world like runpod.io where they are $2 an hour, so $16/hr for 8, that's already half of aws. You can also get a volume discount if you're looking for 96 almost certainly.

An H100 costs about $32k, amortized over 3-5 years gives $1.21 to $0.7 per hour, so adding in electricity costs and cpu/ram etc... runpod.io is running much closer to the actual cost compared to AWS.

> A node of 8 H100s will run you $31.40/hr on AWS, so for all 96 you're looking at $376.80/hr

And what stinks is that you can't even build a Dell/HPE server like this online. You have to 'request a quote' for an 'AI Server'

Going through SuperMicro, you're looking at about $60k for the server, plus 8 GPU's at $25,000 each, so you're close to $300,000 for an 8 GPU node.

Now, that doesn't include networking, storage, racks, electricity, cooling, someone to set that all up for you, $1,000 DAC cables, NVIDIA middleware, downtime as the H100's are the flakiest pieces of junk ever and will need to be replaced every so often...

Setting up a 96 H100 cluster (12 of those puppies) in this case is probably going to cost you $4-5 million. But it should cost less than AWS after a year and a half.

188M input / 80M output tokens per hour was per node I thought?

Reversing out these numbers tells us that they're paying about $2/H100/Hour (or $16/hour for a 8xH100 node).

Disclaimer (one of my sites) https://www.serversearcher.com/servers/gpu - says that a one month commit on a 8XH100 node goes for $12.91/hour. The "I'm buying the servers and putting them in COLO rate" usually works out at around $10/Hour, so there's scope here to reduce the cost by ~30% just by doing better/more committed purchasing.

Ok, so the authors apparently used atlas cloud hosting, which charges $1.80 per h100/hr, which would change the overall cost to around $0.08/ million input and $0.18/million output, which seems much more in line with massive inference margins for major providers.

According to the post their costs were $0.20/1M output tokens (on cloud GPUs), so your numbers are off somewhere.

This is all costs included. Thats 22k tokens per second per node, so per 8 h100's. With 12 nodes they get 264k tokens per second, or 950 million an hour. This get's you to roughly $0.2021 per million at $2 an hour for an h100, which is what they go for on services such as runpod.io . (cheaper if not paying spot-price + volume discounts).

” Our implementation, shown in the figure above, runs on 12 nodes in the Atlas Cloud, each equipped with 8 H100 GPUs.”

Maybe the cost of renting?

I'm curious as well.

Depreciation and GPU failure rate over time must be considered, which I don't see mentioned in the article.

Isn't Blackwell optimized for FP4? This blog post runs Deepseek at fp8, which is probably the sweet spot but new models with fp4 native training and inference would be drastically faster than fp8 on blackwell.

These open models are just commercial binary distributions made available at zero cost with intention to cripple opportunities for Western LLM providers to capitalize on investments.

These are more like really gorgeous corporate swags than FOSS.

Why? Open source isn't in the original title