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Get Started Free →qdrant/qdrant-scaling-qps
Guides Qdrant query throughput (QPS) scaling.
Passed 22 of 23 test cases with the skill, and 15 of 23 without it.
and 10 more in the full report
7 fixed15 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.8-flash on 10/21/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our Qdrant deployment relies on disk storage for vectors because RAM is limited. We are running Linux kernel 5.15. Which Linux asynchronous I/O interface does Qdrant support to significantly reduce disk read bottlenecks during high search throughput?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends enabling io_uring on Linux kernel 5.11 or newer. | Missed | Passed |
The instructions your model reads when the skill loads.
Throughput scaling means handling more parallel queries per second. This is different from latency. Segment count pulls throughput and latency in opposite directions, so pick a priority per collection.
High throughput favors fewer, larger segments so each query touches less overhead.
default_segment_number: 2) Maximizing throughputmemory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older Quantizationindexed_only search parameter, or the experimental prevent_unoptimized optimizer setting (v1.17.1+) Query indexed data onlyoptimizer_cpu_budget to limit indexing CPUs (e.g. 2 on an 8-CPU node reserves 6 for queries)If a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.
replication_factor: 2+ and send requests through a load balancer across all nodes; if every request hits one node, its local replicas serve the query and the other replicas sit idle Deploy behind a load balancerSee also Horizontal Scaling for general horizontal scaling guidance.
If it is not possible to keep all vectors in RAM, disk I/O can become the bottleneck for throughput. In this case:
storage.performance.io_uring: auto on Qdrant 1.19 or newer, async_scorer: true on 1.18 or older. Requires Linux with a kernel that supports io_uring Async I/Otest cases passed, vs 15 / 23 without
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