In my experience, heavy number crunching is more suitable to run on dedicated machines, rather than virtualized "cloud" cores. More consistent performance, no noisy neighbors and cheaper in the long term.
"The cloud" simply means remote servers. Those servers may or may not contain application-specific hardware acceleration.
Nowadays many cell phone application processors also have dedicated hardware to accelerate neural nets, but they will always be limited by thermal constraints.
Citation needed :)) There are economies of scale and various optimizations that are just not possible with dedicated machines.
Anecdotal evidence: a company I worked in had a dedicated DC with hundreds/thousands of machines that mostly ran SQL queries on petabytes of data (any query would take ~5-30 minutes). Eye-watering budget and whole teams to maintain the cluster...
They switched to GCP/BigQuery, got queries that ran in seconds at a fraction of the budget.
Those economies are for the cloud provider, not you. The cost to you is set at just less than their best guess at what you think self-hosting will cost you.
Cloud hosting only makes sense in a case that your capacity needs are changing rapidly and unpredictably, or the case where you’re so big that the cloud hosting company is effectively a department. Any other time it will almost certainly be cheaper to self host.
If you can saturate it, sure. The average person is not going to be running 24/7 AI compute tasks though, so paying for a month of A100 dedi when you only need 10-20 minutes of compute/week is a complete waste.
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In my experience, heavy number crunching is more suitable to run on dedicated machines, rather than virtualized "cloud" cores. More consistent performance, no noisy neighbors and cheaper in the long term.
"The cloud" simply means remote servers. Those servers may or may not contain application-specific hardware acceleration.
Nowadays many cell phone application processors also have dedicated hardware to accelerate neural nets, but they will always be limited by thermal constraints.
Citation needed :)) There are economies of scale and various optimizations that are just not possible with dedicated machines.
Anecdotal evidence: a company I worked in had a dedicated DC with hundreds/thousands of machines that mostly ran SQL queries on petabytes of data (any query would take ~5-30 minutes). Eye-watering budget and whole teams to maintain the cluster... They switched to GCP/BigQuery, got queries that ran in seconds at a fraction of the budget.
This anecdote is the opposite of the ones I usually hear.
Economies of scale go in the other direction usually.
Those economies are for the cloud provider, not you. The cost to you is set at just less than their best guess at what you think self-hosting will cost you.
Cloud hosting only makes sense in a case that your capacity needs are changing rapidly and unpredictably, or the case where you’re so big that the cloud hosting company is effectively a department. Any other time it will almost certainly be cheaper to self host.
Sounds like they could have switched to Clickhouse if they already had the hardware.
The inverse is also true; I don't have to pay Google or AWS to deploy my software to dedicated consumer devices. The user already paid for it.
If you can saturate it, sure. The average person is not going to be running 24/7 AI compute tasks though, so paying for a month of A100 dedi when you only need 10-20 minutes of compute/week is a complete waste.