The settings that matter most depend on the job. For uploads, start with --drive-chunk-size and --transfers; for recursive copy, sync, or inventory work, test --fast-list and consider --checkers; for a mount, tune directory caching, polling, per-file buffering, and VFS cache behavior. Every faster setting consumes a resource—usually RAM, API transactions, local disk, or freshness—so there is no single best profile for every Google Drive workload.
Start with the setting that matches your workload
Google Drive performance problems usually come from a mismatch between the operation and the knob being changed. Uploading large files is governed by chunk size and the number of simultaneous transfers. Walking a large tree is dominated by directory-listing API calls. A mounted remote has two separate concerns: how quickly directory metadata is refreshed and how file data is buffered or cached locally.
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| Workload | Settings to examine first | Primary trade-off |
|---|---|---|
| Uploading files | --drive-chunk-size, --transfers |
Higher throughput can require substantially more RAM. |
| Recursive copy, sync, or inventory | --fast-list, then --checkers |
Fewer listing transactions require more memory; more checkers can add request pressure. |
Browsing an rclone mount |
--dir-cache-time, --poll-interval |
Longer caching reduces repeated listings but delays visibility of outside changes. |
| Reading files through a mount | --buffer-size, VFS cache mode, read-chunk options |
RAM and local-disk use rise with open files and cache requirements. |
The defaults and backend behavior can change between rclone releases. Confirm the current Google Drive backend documentation, global flags documentation, and mount documentation for the version you run.
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What --drive-chunk-size controls
Drive uploads are sent in chunks. The documented default is 8Mi. The Drive backend says that making the chunk larger can improve performance, but one chunk is buffered in memory for each active transfer. Valid values must be powers of two and at least 256Ki. The backend description is explicit about the trade-off: “Making this larger will improve performance, but note that each chunk is buffered in memory one per transfer.” See the Drive backend option reference for the current syntax and limits.
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A larger chunk can reduce protocol overhead and make better use of a fast connection, particularly for a few large files. It is less likely to transform a workload made up of thousands of tiny files, where metadata operations and file-open overhead can dominate. Do not assume that the largest accepted value is fastest: the useful point depends on file sizes, round-trip latency, upload bandwidth, and available RAM.
How --transfers changes the result
The global --transfers flag sets the number of files transferred concurrently and defaults to 4. More transfers can keep a high-bandwidth connection busy when individual files are slow, but every active upload can hold a Drive chunk in memory. Increasing chunk size and transfers at the same time multiplies the memory requirement rather than adding a small fixed amount.
As a planning bound, think of upload chunk memory as approximately drive_chunk_size × active_transfers, with additional memory required by rclone, the operating system, and file metadata. This is not a promise of exact process usage; it is a way to spot an unsafe combination before testing. If the machine starts swapping, the apparent network gain can disappear.
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- Record a baseline with the documented defaults, for example
rclone copy /data drive:backup. Note elapsed time, files per second, network utilization, and peak process memory. - Change one variable first. Test a larger power-of-two chunk such as
16Miwhile leaving--transfers 4unchanged:rclone copy /data drive:backup --drive-chunk-size 16Mi --transfers 4. - If memory remains comfortable and the connection is still underused, test a higher transfer count separately. Keep the chunk fixed so you can tell which change helped.
- Stop increasing a setting when throughput stops improving, memory pressure appears, or Drive/API errors become more frequent. Keep the fastest stable combination, not the most aggressive one.
The example values above are test points, not universal recommendations. A computer with limited RAM may need the 8Mi default or a lower transfer count; a machine handling large files on a fast, low-latency link may benefit from larger chunks.
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Choosing for file shape
- A few large files: chunk size and transfer concurrency are usually the first measurements to make.
- Many small files: raising transfers may help only until listing, metadata, or local filesystem overhead becomes the bottleneck. A huge chunk does not make a tiny file large.
- Several jobs sharing one host: budget RAM across all rclone processes. Four transfers in one command can become eight or more when multiple jobs run together.
Making recursive copy, sync, and listing work faster
Use --fast-list when API transactions are the bottleneck
The Drive backend supports --fast-list, which combines directory-listing requests. Rclone describes it as using fewer transactions in exchange for more memory. It is most relevant when a command must walk many directories before it can decide what to copy or synchronize.
Current rclone documentation includes an undated illustrative example: a tree containing 10,600 directories and 39,000 files took 22:05 without --fast-list and 58 seconds with it. That is a documented workload example, not a guaranteed result for your Drive account, network, or rclone version.
Try it on a representative dry run or a non-destructive listing:
rclone size drive:large-tree --fast-listIf the command approaches the host’s memory limit, the transaction reduction may not be worth it. Without enough RAM, paging can make the supposedly faster mode slower than the default.
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Understand --checkers
The global --checkers flag controls parallel checking and defaults to 8. On Drive, the fast recursive listing implementation uses the --checkers value for parallel requests. More checkers can reduce idle time on a high-latency link, but they also create more simultaneous API work and are not a guaranteed throughput increase.
Do not confuse --checkers with --transfers: checkers compare or discover files, while transfers move file data. For example:
rclone sync /data drive:backup --fast-list --checkers 8 --transfers 4Change the checker count only after observing whether listing, API rate limits, CPU, or memory is the limiting factor. Keep transfer concurrency independent so a listing experiment does not accidentally alter upload behavior. The definitions and defaults are in the global flags reference.
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When --fast-list is a poor fit
- RAM is already close to full before the scan starts.
- The tree is small enough that listing calls are not a meaningful part of elapsed time.
- You run several large scans simultaneously and cannot reserve memory for each one.
- The operation is dominated by uploading or downloading bytes rather than discovering paths.
Tuning an rclone mount: metadata first, file data second
Directory freshness with --dir-cache-time
--dir-cache-time controls how long directory entries are treated as current and defaults to five minutes. A longer period reduces repeated directory listings while browsing, but a change made outside the mount may not appear until the cache expires or a supported remote reports the change.
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For remotes that support change polling, --poll-interval must be shorter than --dir-cache-time. Polling is not a universal Drive refresh switch: it applies only where the backend supports it. A simple mount with the documented five-minute cache and a shorter polling interval is:
rclone mount drive: ~/drive --dir-cache-time 5m --poll-interval 1mChoose the relationship based on your tolerance for stale listings. If external changes must appear quickly, shorten the cache and use a compatible polling interval; expect more metadata work. If the mount is mostly read-only and directories are stable, a longer cache can avoid repeated API calls. The interaction and backend limitations are described in the rclone mount documentation.
Per-file buffering with --buffer-size
VFS --buffer-size is an upper bound for each open file, not a single pool shared by the mount. If an application opens many files concurrently, the possible buffer allocation grows roughly as buffer_size × open_files. A mount used by a media indexer, build system, or database-like application can therefore consume far more RAM than an interactive shell opening one file at a time.
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rclone mount drive: ~/drive --buffer-size 8MiThat 8Mi value is an example for measurement, not a claim that it is optimal. If raising it does not improve observed reads, return to a smaller value. If the process or system begins swapping, lower it or reduce concurrent access.
VFS file caching and local disk
VFS cache modes can improve filesystem compatibility by keeping file data on local storage, but they consume disk space and add cache-management behavior. The mount documentation notes that reading and writing a file simultaneously requires VFS caching. Cache mode is therefore a compatibility choice tied to the application’s access pattern, not a universal speed switch.
- Read-mostly browsing: start with the least caching that your application accepts, then measure repeated reads.
- Applications that reopen or update files: use a cache mode that satisfies their access requirements and reserve sufficient local disk.
- Small local disks: avoid assuming that more aggressive caching is harmless; cache growth can become the new bottleneck.
VFS read chunks: useful idea, no proven Drive optimum
VFS read-chunk options can issue multiple chunks concurrently. That can help when latency is high or bandwidth is plentiful, because one outstanding request may otherwise leave the connection idle. The optimum depends on backend behavior, latency, file size, and how many files are open.
Rclone’s published scaling example for concurrent chunk reads is for an S3 backend, not Google Drive. Its measured values must not be presented as a tested Drive recipe. The official mount documentation therefore supports treating read-chunk settings as experiments: change one value, use files and access patterns that resemble production, and compare throughput, latency, RAM, and API stability. Do not import an S3-specific VFS profile and assume it is optimal for Drive.
HTTP/2: leave the documented default alone unless testing deliberately
In the documented Drive backend version, HTTP/2 is disabled by default because of an unresolved issue. The backend exposes --drive-disable-http2 to control that behavior. Because this detail is release-dependent and the default exists for a reason, do not re-enable HTTP/2 as a generic performance tweak. Check the current Drive documentation for your installed release before changing it, and test any alternative with a workload that can be safely repeated.
A measurement workflow that avoids misleading “optimizations”
- Classify the operation. Decide whether elapsed time is mostly upload/download data, recursive discovery, or mounted access. Do not tune a mount buffer to solve an API listing bottleneck.
- Capture a baseline. Record rclone version, command, data shape, elapsed time, transfer rate, peak RAM, CPU, local-disk use, and any API or filesystem errors.
- Use representative data. Include the actual mix of large and small files, directory depth, and number of concurrently open files. A synthetic single-file test cannot predict a 40,000-file sync.
- Change one setting. Keep chunk size fixed while testing transfers; keep transfers fixed while testing checkers; keep cache settings fixed while testing read behavior.
- Watch the constrained resource. Faster network utilization is not an improvement if the host swaps, the local cache fills, or API throttling increases.
- Repeat the winning test. Run it more than once and at comparable times. Keep a stable result, not a one-off peak.
- Document the reason. Record the workload, memory budget, cache-disk budget, and freshness requirement alongside the flags so a future rclone upgrade can be re-evaluated.
Practical starting profiles
| Situation | Starting approach | What to monitor |
|---|---|---|
| Large-file uploads on a RAM-rich host | Keep --transfers 4; test one larger power-of-two --drive-chunk-size at a time. |
Peak RAM, sustained upload rate, and stability. |
| Many-directory sync with available RAM | Test --fast-list; leave --checkers at 8 initially. |
Scan time, memory growth, and API errors. |
| Many-directory sync on a memory-constrained host | Use the normal listing mode first; change checkers only if listing latency is demonstrably dominant. | Swap activity and total elapsed time. |
| Interactive mount with occasional outside changes | Set --dir-cache-time to the freshness you can tolerate and keep --poll-interval shorter when polling is supported. |
How quickly changes appear and how often directories are re-listed. |
| Mount with many simultaneous readers | Use a conservative --buffer-size; estimate the per-open-file aggregate before increasing it. |
Open-file count, RAM, swapping, and read latency. |
Common mistakes and how to correct them
- Raising every concurrency flag together: separate
--transfersfrom--checkersand test them for their respective jobs. - Using a huge chunk because the link is fast: calculate memory for every active transfer and include other rclone jobs.
- Expecting
--fast-listto speed every command: it trades memory for fewer listing transactions and helps mainly on large recursive trees. - Setting a long directory cache while expecting instant external updates: shorten the cache or accept the documented delay; polling is only available on supported remotes.
- Treating
--buffer-sizeas total mount memory: multiply the bound by possible concurrent open files. - Copying an S3 VFS recipe to Drive: the cited chunk-read measurements are backend-specific and do not establish a Google Drive optimum.
- Disabling safeguards for speed without stating the cost: checksum, metadata, and other integrity options change what rclone verifies; do not remove them as blanket advice.
Bottom line
For Google Drive uploads, measure --drive-chunk-size and --transfers together with RAM usage. For large recursive operations, --fast-list is the major listing option when you can afford its memory cost, while --checkers controls parallel checking rather than file-data concurrency. For mounts, set directory cache and polling from your freshness requirement, then size --buffer-size for the number of open files and choose VFS caching for compatibility and disk capacity. The official documentation does not establish one universally optimal profile or a Google Drive-specific best value for VFS read chunks, so workload-based measurement is the reliable final step.
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