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[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Let’s run some benchmarks"
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[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Let’s run some benchmarks"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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] | p | We can use the glove-100-angular and scripts from the vector-db-benchmark project to upload and query the vectors. | [
"articles",
"articles/memory-consumption"
] | 3,573 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Let’s run some benchmarks"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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] | p | With the first run we will use the default configuration of Qdrant with all data stored in RAM. | [
"articles",
"articles/memory-consumption"
] | 3,574 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Let’s run some benchmarks"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | After uploading vectors, we will repeat the same experiment with different RAM limits to see how they affect the memory consumption and search speed. | [
"articles",
"articles/memory-consumption"
] | 3,575 |
[
"Minimal RAM you need to serve a million vectors - Qdrant"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(1) | [
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] | p | This suggests that about 1.2Gb of memory is needed to serve around 1 million vectors, and there is no speed degradation when limiting memory usage above 1.2Gb. | [
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"articles/memory-consumption"
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[
"Minimal RAM you need to serve a million vectors - Qdrant"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2) | [
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] | p | In the second experiment, we tested how well our system performs when vectors are stored using the memory-mapped file (mmap). | [
"articles",
"articles/memory-consumption"
] | 3,583 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Vectors stored using MMAP"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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"articles",
"articles/memory-consumption"
] | 3,588 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Vectors stored using MMAP"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18) | [
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] | p | In the third experiment, we tested how well our system performs when vectors and HNSW graph are stored using the memory-mapped files. | [
"articles",
"articles/memory-consumption"
] | 3,591 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Vectors and HNSW graph stored using MMAP"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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] | p | Initially, we tested on a network-mounted disk, but its performance was too slow, with a read IOPS of 6366 and a bandwidth of 24.9 MiB/s: | [
"articles",
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] | 3,599 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"How to speed up the search"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25) | [
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[
"Minimal RAM you need to serve a million vectors - Qdrant",
"How to speed up the search"
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] | p | Let’s see how these results translate into search speed: | [
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"articles/memory-consumption"
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[
"Minimal RAM you need to serve a million vectors - Qdrant",
"How to speed up the search"
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[
"Minimal RAM you need to serve a million vectors - Qdrant",
"How to speed up the search"
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[
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[
"Minimal RAM you need to serve a million vectors - Qdrant",
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"Conclusion"
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"articles",
"articles/memory-consumption"
] | 3,612 |
[
"Minimal RAM you need to serve a million vectors - Qdrant",
"Conclusion"
] | /articles/memory-consumption/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32) | [
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"Question Answering as a Service with Cohere and Qdrant - Qdrant"
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] | p | That might be definitely a blocker for any semantic | [
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"articles/qa-with-cohere-and-qdrant"
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[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
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"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
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"Question Answering as a Service with Cohere and Qdrant - Qdrant",
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[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
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"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
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"Question Answering as a Service with Cohere and Qdrant - Qdrant",
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"articles",
"articles/qa-with-cohere-and-qdrant"
] | 3,647 |
[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
] | /articles/qa-with-cohere-and-qdrant/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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] | p | customize the embedding model with your own data. | [
"articles",
"articles/qa-with-cohere-and-qdrant"
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[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Why co.embed API and Qdrant go well together?"
] | /articles/qa-with-cohere-and-qdrant/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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"articles",
"articles/qa-with-cohere-and-qdrant"
] | 3,653 |
[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"System architecture overview"
] | /articles/qa-with-cohere-and-qdrant/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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"articles",
"articles/qa-with-cohere-and-qdrant"
] | 3,654 |
[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"System architecture overview"
] | /articles/qa-with-cohere-and-qdrant/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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"articles",
"articles/qa-with-cohere-and-qdrant"
] | 3,660 |
[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Looking for the correct answer"
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[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
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[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
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] | p | We don’t want to maintain our own service for the neural encoder, nor even set up a Qdrant instance. | [
"articles",
"articles/qa-with-cohere-and-qdrant"
] | 3,665 |
[
"Question Answering as a Service with Cohere and Qdrant - Qdrant",
"Implementing the QA search system with SaaS tools"
] | /articles/qa-with-cohere-and-qdrant/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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Subsets and Splits