Deploying DeepSeek on 96 H100 GPUs

Maybe the cost of renting?

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

These are more like really gorgeous corporate swags than FOSS.

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.

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.

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.

> 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.)

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?

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.

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

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