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Comment by dig6x | Hacker News Reader

"...600 billion parameters using automatic sharding. We demonstrate that such a giant model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art."

It does appear that at the initial, resource intensive stages of tech like NLP big tech is primed to pave the way. We saw this happen across cloud, AI more generally, storage etc. But big tech then begins focusing on making the tech accessible to industry value chains (Azure, AWS, Amazon's AI services etc.). But as the industry matures there's more room for specialized startups/companies to enter the space to capture lucrative niches - thats exactly what Snowflake did for Cloud.

Definitely see this kind of scale as a step toward a more robust, mature industry if anything. Better it move forward than not.

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sounds interesting. can you elaborate? Not familiar with what Snowflake does or how it compares. Thanks

Similarly, not familiar with the unit economics when it's sitting on top of S3 / Azure storage. But it's growing really quick so it must be Something.

Launched in 2014, its basically a purpose-built SQL cloud data warehouse solution. Its success pivoted among other factors, on its ability to abstract compute power and data storage to create a modular solution that could be made efficient for any data warehousing configuration.

In 2013 AWS augmented its core cloud offering with the introduction of Redshift, a ‘data warehousing as a service’ solution. The Redshift solution bundled compute and storage, reducing the ability to meet individual customer needs to scale either component separately in a cost efficient manner. Not having the option to unbundle compute and storage was inconsistent with the flexible nature that cloud had become known for.

Snowflake’s solution separated storage, compute, and services into separate layers, allowing them to scale independently and achieve greater cost efficiencies. By offering flexibility it was able to better address the requirements of a wider range of customers - who had previously been limited to the more restrictive bundled options, like Redshift.

ic. but from Amazon's perspective, if customers want something that is mostly turn-key with the ability to customize, wouldn't they just combine AWS services themselves? I would believe Amazon has DB only solutions, compute only solutions like EC2 etc... So why was Snowflake able to thrive in this environment? Was the market simply too big?

Yeh the CLoud market was at a stage where the niche with some convenience add could thrive. Now we're seeing all these multi cloud platforms emerge because enterprises are managing multiple server providers at once etc. so you can imagine all the opportunities for horizontal scaling beyond big tech in the industry.

What snowflake offers would cost you millions in engineering time to recreate from AWS primitives from scratch. AWS does offer a competing all-in-one offering (Redshift) but Snowflake has a superior cost structure and has outpaced it on features.

What is the cost when getting all data from network compared to local disks ? Is this connected to the cloud not offering local persistent disks ? Will this still work in colocation ?

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