mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-01 00:48:32 +02:00
margin after summary tags, removed outdated br tags (#988)
This commit is contained in:
@@ -208,8 +208,6 @@ Now the out-of-memory happens when we allow using **600mb** RAM only
|
||||
|
||||
</details>
|
||||
|
||||
<br/>
|
||||
|
||||
At this point we have to switch from network-mounted storage to a faster disk, as the network-based storage is too slow to handle the amount of sequential reads that our system needs to serve the queries.
|
||||
|
||||
But let's first see how much RAM we need to serve 1 million vectors and then we will discuss the speed optimization as well.
|
||||
@@ -251,8 +249,6 @@ With this configuration we are able to serve 1 million vectors with **only 135mb
|
||||
|
||||
</details>
|
||||
|
||||
<br/>
|
||||
|
||||
At this point the importance of the disk speed becomes critical.
|
||||
We can serve the search requests with 135mb of RAM, but the speed of the requests makes it impossible to use the system in production.
|
||||
|
||||
|
||||
@@ -26,7 +26,5 @@ Here are the principles we followed while designing these benchmarks:
|
||||
|
||||
</details>
|
||||
|
||||
</br>
|
||||
|
||||
Some of our experiment design decisions are described in the [F.A.Q Section](/benchmarks/#benchmarks-faq).
|
||||
Reach out to us on our [Discord channel](https://qdrant.to/discord) if you want to discuss anything related Qdrant or these benchmarks.
|
||||
|
||||
@@ -1262,7 +1262,6 @@ await client.GetCollectionInfoAsync("{collection_name}");
|
||||
```
|
||||
|
||||
</details>
|
||||
<br/>
|
||||
|
||||
If you insert the vectors into the collection, the `status` field may become
|
||||
`yellow` whilst it is optimizing. It will become `green` once all the points are
|
||||
|
||||
Reference in New Issue
Block a user