banana_giraffe
Joined 12 years ago
Joined 12 years ago
No idea if it's good enough for all podcasts that are out there. It works for me and the podcasts I listen to, and I have tried testing it against a larger corpus, but I'm one developer and I just wanted to solve a problem for me.
SELECT \* from read_csv('example.csv');
Writing generally involves reading to an in-memory database, making whatever changes you want, then something like COPY new_table TO 'example.csv' (HEADER true, DELIMITER ','); Raptor Lake + 5080: 380.63 GB/s
Raptor Lake (CPU for reference): 20.41 GB/s
GB10 (DGX Spark): 116.14 GB/s
GH200: 1697.39 GB/s
This is a "eh, it works" benchmarks, but should give you a feel for the relative performance of the different systems.In practice, this means I can get something like 55 tokens a sec running a larger model like gpt-oss-120b-Q8_0 on the DGX Spark.
https://gist.github.com/Q726kbXuN/834882f59bc921a3865272a66d...
"Troubleshooting / Debugging" is meant for the traditional questions, "Tooling recommendation", "Best practices", and "General advice / Other" are meant for the soft sort of questions.
I have no clue what the engagement is on these sort of categories, though. It feels like a fix for a problem that started years ago, and by this point, I don't really know if there's much hope in bringing back the community they've worked so hard to scare away. It's pretty telling just how much the people that are left hate this new feature.
[1] https://meta.stackoverflow.com/questions/435293/opinion-base...
The default Lambda quota for all accounts is 10240 MB. I've never seen it below that (in recent memory, at least), even on fresh accounts not connected to a big org.
I know I routinely use 10gb of RAM for my account that's never talked to support for the related CPU allocation.
Nope, they don't eat up space. One of the fun side effects of sparse files on NTFS is calling WriteFile on the sparse file can lead to an ERROR_DISK_QUOTA_EXCEEDED error, which applications tend to not expect.
There was a prototype of a more advanced model that would be closer to what you're describing, but it never made it to market.
But that was a few months ago, so for all I know they've tightened down more hatches since then.