161–170 of 285 posts
Earlier quoted context omitted.
supposedly, the next step is into fiber & optics. but I generally agree, people put a lot of faith in the exponential leaps vs the exponential space. You tell them we're not living on mars any time soon and they'll bring up christopher columbus.
How would fiber and optics help if the bottleneck is computation efficiency and not data transfer?
"Secondary market data shows moderately-used 2 to 3 year old GPUs trading at 50% to 70% of new pricing under normal conditions." That's for good NVidia H100 units.[1] There's a shortage of those. That seems to be the price after removal, cleaning, testing and refurbishing. Raw units removed from a shutdown will not be as valuable. H100 units are available on eBay, but multiple sellers are using the same picture of a…
If you search ebay for server equipment like a Dell R840 with 768GB RAM, the same sort of dealers who are selling that and have thousands of feedback (at 98.5% of greater rating) are the ones I would consider much less risk.
Essentially nothing, fractions of a penny on a dollar, because players who could afford paying real money won't risk the crusty old hardware - so you're limited to buyers who still need a massive cluster but don't have AI infinite money glitch enabled
Are failing GPUs really a big issue? I don't know how they behave when they die, but if it can be detected quickly, the affected nodes can just be removed from the pool.
I built and operated an application that used 1-2000 L4 GPUs in production for a couple of years. Long-term GCP reservations running in a GKE cluster. At that scale we had a few GPU failures per week, and once saw 3 in a day. Nearly every failure required an engineer to manually intervene to get rid of the bad node, and then file a ticket with GCP support as they requested.
NVIDIA GPUs throw an “XID” code when they fail, which can be seen from serial port logs for GCP compute nodes (not through k8s!). If you’re lucky, it fires right as your application starts to fail, but there’s often a delay of several minutes. Even when you get an XID, by default GKE only responds to one or a few of them. You can expand the list via configuration, but the reconciliation loop is so slow that might still take 10-15 minutes during which a pod is puking errors and someone might be getting paged.
They’re working on it, and we never saw a single XID after we migrated to H100s, so the situation is improving. I imagine other clouds are even worse, though iirc Azure was leading some effort to improve k8s node problem detector to include accelerator problems so maybe they have a better story.
Training workloads are rather more sensitive to a node failure given that many modern training runs (including SFT etc) need multiple nodes where topology matters, and they might not have another e.g. 8xH100 box in the right place when one fails.
I don't get how this is unique to GPU clusters. As a general rule, underwriters are not qualified to operate and maintain the assets they underwrite loans for. That's why houses, cars, equipment, ... go at auction at a fraction of their value. And why lenders have insurance.
It isn't, and you're right. This is just a long-winded article by someone who thinks they've come across a deep, crucial insight.
I witnessed a bank foreclosure stemming from large, unpaid loans to a lumber mill. The bank absolutely didn't want to take possession of the operation, but had no choice when the founder decided to call it a day.
The bank had no idea what to do with finished lumber sitting in the drying ovens, let alone the entirety of mill infrastructure itself. After struggling to find a buyer, they hired the founder as a consultant to handle liquidation. The same would happen with a repossessed datacentre.
All indications are there will be a lot of repossessed GPUs appearing on the market before too long. Likely to be messy for a while but will open up a lot of possibilities when it’s easy to get your hands on some secondhand GPUs.
This sounds like weapons market after the crash of soviet republic. Never has been a better time to get a functioning tank or parts of nukes.
I don't get how this is unique to GPU clusters. As a general rule, underwriters are not qualified to operate and maintain the assets they underwrite loans for. That's why houses, cars, equipment, ... go at auction at a fraction of their value. And why lenders have insurance.
All indications are there will be a lot of repossessed GPUs appearing on the market before too long. Likely to be messy for a while but will open up a lot of possibilities when it’s easy to get your hands on some secondhand GPUs.
Sincerely, perhaps there will be a new flood of crypto farms, which I suppose would drive the prices of crypto down. Other ideas for what changes are enabled by massive supply of cheap GPUs?
And I feel that AI hardware is going to rapidly depreciate faster than even typical PC hardware
Earlier quoted context omitted.
They're pretty specific to the datacenter use case with no outputs and they need to be cooled externally, principally through the very loud high speed fans used in data centers. I suppose you could strap a fan to one and put it in a normal case or maybe make a dedicated after market cooler (like the water blocks made for water cooling cases).
> Very loud high speed fans if you haven't heard a 5u server intended for a datacenter rack come to life it's quite the experience. Sounds like a plane taking off.
Earlier quoted context omitted.
Why would they be shot? Unlike the consumer cards that are basically factory overclocked to look good on benchmarks, the datacenter GPUs are designed to run at full tilt 24/7 and survive for years.
According to the article these cards have a 9% annual failure rate.