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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Do I need an object cache, or do I need a faster object store? The answer depends on whether your current object store is actually the bottleneck and whether your workload benefits from keeping a small, frequently reused set of objects close to applications. A faster durable store can remove the need for a separate cache in some cases—but MinIO’s benchmark demonstrates one configuration, not a universal rule. Here’s how to assess your own workload and what MinIO’s results do and do not show.
What MinIO’s benchmark demonstrates
MinIO reports that an eight-node AIStor cluster delivered 267.8 GiB/s of GET throughput and 120 GiB/s of PUT throughput in its published benchmark. That is evidence that a durable object store can deliver very high throughput in a particular setup; it is not a prediction for other hardware or a general ranking of object storage against caching. MinIO’s benchmark account is vendor-published, and no independent reproduction of this exact test is established here.
The tested configuration
MinIO describes each storage node as having one Intel Xeon 6781P, 504 GB of DDR5 memory, two 400 GbE network interfaces, and 24 Solidigm D5-P5336 122.88 TB QLC drives. Across eight nodes, the article reports roughly 24 PB of raw capacity. The cluster used EC:4 across 24 erasure sets of eight drives. Eight Warp clients generated the workload, using 256 MiB objects and 32 concurrent connections per client. MinIO reports zero errors over 16 phases.
The reported 267.8 GiB/s GET and 120 GiB/s PUT results belong to that setup and workload. They should not be treated as an expected result for a different drive count, network, object size, client configuration, or application request pattern.
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Why it is not a controlled cache-versus-store contest
MinIO’s article also cites CoreWeave cache figures, but says the tests differ in node roles, object sizes, and client counts. That comparison is not a controlled head-to-head. It cannot establish that object storage generally outperforms caches, or that a cache is never useful.
MinIO’s benchmarking documentation describes other published results too, but they are separate tests and should not be combined with the article’s figures:
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| Published result | Reported figures | How to interpret it |
|---|---|---|
| Supermicro, Intel, and MinIO solution brief, summarized in MinIO’s benchmarking documentation | 229 GiB/s average GET and 118 GiB/s average PUT; a separately reported peak second reached 237 GiB/s GET and 126 GiB/s PUT. | A distinct eight-node result. Do not substitute these averages or peak values for the 267.8/120 GiB/s benchmark. |
| Solidigm, MinIO, and Intel test, summarized in MinIO’s benchmarking documentation | 268 GiB/s peak GET and 120 GiB/s peak PUT. | A distinct published test of roughly 24 PB raw across eight nodes, not the exact 267.8 GiB/s result from the article. |
MinIO’s benchmarking documentation attributes these results to their respective published sources. Their differing hardware and test conditions matter as much as the headline numbers.
How to tell whether the object store is your bottleneck
Start with the application’s actual requirements, not a cache product or a large-object throughput number. Determine the throughput and latency the application needs, including its acceptable tail latency, then measure a representative workload against those targets.
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Build a representative baseline
Use the application’s distribution of object sizes, read/write mix, concurrency, and request types. If production includes writes, deletes, or metadata operations alongside reads, include them in testing rather than benchmarking only sequential GETs. Vary concurrency and run tests long enough to identify saturation or unstable performance. Record latency as well as throughput; a system that meets an aggregate bandwidth target can still miss an application’s latency requirement.
MinIO’s benchmark guidance covers performance testing across object, drive, network, replication, and client paths. Its Warp tool supports workload simulation and distributed clients; see the Warp project and Warp README.
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Isolate the constrained path
Once you have a baseline, test likely limits independently: drive throughput, network bandwidth, client capacity, and object-operation performance. If a client cannot issue enough requests, a faster store may not change application performance. If the network is saturated, adding a cache on the same constrained path may not help either. Repeat measurements under representative cluster health and load, then compare each result with the application’s throughput and latency targets.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a cache can help—and when it may not
A separate cache is most plausible when access repeatedly concentrates on a small hot working set and the cache can materially improve the latency or throughput the application experiences. It is less compelling when requests span much of the dataset, the durable store already meets the application’s requirements, or the cache does not relieve the actual constrained path.
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Make the decision across the workload’s real requirements:
- Latency: Does direct access meet the application’s tail-latency needs?
- Locality and reuse: Do requests repeatedly revisit a small set of objects, or range broadly across the dataset?
- Throughput and concurrency: Does the measured system satisfy the actual object sizes and request mix at production concurrency?
- Freshness and correctness: What should happen after an object changes, and how will cached data be updated or invalidated?
- Operational cost: Account for cache capacity, memory or SSD hardware, additional network paths, and the work of maintaining correct data. No comparative cost figures are established here.
MinIO’s AIStor Cache is a distinct distributed shared memory pool for frequently accessed objects. MinIO says it retrieves objects automatically on cache misses and updates cached versions when objects change. Those behaviors are product-specific; they do not remove the need to define freshness and correctness requirements for your own architecture. See MinIO’s AIStor Cache documentation.
Do not confuse storage-device caches with an object cache
A drive- or controller-layer cache is not the same thing as an application-visible distributed object cache. In its deployment guidance, MinIO says, “MinIO recommends against caching, either at the drive or the controller layer.” The guidance warns that cache fills and clears can cause I/O spikes and unpredictable performance in the deployment configuration it describes; it is not a blanket finding about every cache design. See MinIO’s distributed-deployment guidance.
MinIO’s vendor-authored benchmark article says, “A cache should be faster than what’s behind it.” Treat that as the article’s framing, not as a universal technical standard: a cache is useful only if it improves the path that limits the workload, while preserving the application’s required freshness and correctness.
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A practical decision sequence
- Write down the target. Define the throughput, tail latency, and correctness behavior the application requires.
- Measure the real request pattern. Test representative object sizes, reads and writes, concurrency, and other material operations. Measure both latency and throughput.
- Find the constrained path. Separate drive, network, client, and object-operation limits using appropriate tests.
- Test the proposed change. Compare direct object-store access with the cache design under the same representative workload and cluster conditions; evaluate freshness behavior as well as performance.
- Keep the cache only if the result justifies it. A cache should address a measured bottleneck and provide enough benefit to warrant its hardware and operational complexity.
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