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@@ -52,7 +52,7 @@ Our Qdrant configuration differed in the ways that matter for disk-rescore perfo
We used TurboQuant 4-bit for Qdrant, our current best-practice encoding for this recall band, not a bit-identical reproduction of their quantization choice. We tested replication factor \= 1 because we wanted to see the lowest footprint we could reasonably leverage to beat Elastic’s results.
While we could have pushed the infrastructure footprint even lower to maximize throughput, benchmarking disk-based setups for a dataset of this size is largely an academic exercise. Even without compression, using `fp16` representation, this entire corpus and its index fits on a single `[rc6.2xlarge](https://instances.vantage.sh/aws/ec2/r6g.2xlarge?currency=USD)` instance, which would cost roughly $295/mo with on-demand pricing. In a real-world deployment, our users wouldn't even bother with disk here, they would simply run it in-memory for maximum performance.
While we could have pushed the infrastructure footprint even lower to maximize throughput, benchmarking disk-based setups for a dataset of this size is largely an academic exercise. Even without compression, using `fp16` representation, this entire corpus and its index fits on a single [rc6.2xlarge](https://instances.vantage.sh/aws/ec2/r6g.2xlarge?currency=USD) instance, which would cost roughly $295/mo with on-demand pricing. In a real-world deployment, our users wouldn't even bother with disk here, they would simply run it in-memory for maximum performance.
Queries were driven from a separate AWS `c5.2xlarge` instance (8 vCPU, 16 GiB, us-east-1) running our open harness against the Qdrant cluster over the network; the same client-and-server separation Elastic used with Jingra.