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A model can finish generating quickly while the workflow still takes time to decode, write, sync, or process its output. In Dakota Lin’s small local Python experiment, forcing os.fsync changed the measured write span—but the result is a single fixture capture, not a general benchmark or proof that disk writes are usually the bottleneck.
What the experiment measured
In a September 17, 2026 Dev.to article, Dakota Lin timed a small workflow that loaded a JSON fixture, decoded it, wrote two files into a throwaway directory, and included a post-write pause. The timing was divided into named spans so that remote connection and first-byte time, streamed generation, local decoding and writes, and work after the write would not be conflated.
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The comparison changed whether the file-writing path forced a sync. Lin used time.perf_counter() for elapsed durations. The reported values below are from one local fixture capture by Lin in 2026; they are sample output, not independently verified results or expected timings on another computer.
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| Measure | Without forced sync | With forced sync |
|---|---|---|
| Write span | write_nofsync: 3.9 ms, one local fixture capture by Dakota Lin, 2026 |
write_and_fsync: 41.6 ms, one local fixture capture by Dakota Lin, 2026 |
| Total measured workflow | 25.2 ms, one local fixture capture by Dakota Lin, 2026 | 62.7 ms, one local fixture capture by Dakota Lin, 2026 |
These numbers show what happened in that run, not a universal fsync penalty. The experiment did not use production traffic, controlled repeated trials, or a cross-machine comparison; it also did not trace editor internals, remote GPU scheduling, prompt-cache warmth, or variation from other users on a free server.
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Why a buffered write and fsync are different measurements
A normal buffered write can return before the data has been forced to disk. Python documents os.fsync(fd) as forcing a file descriptor’s data to disk. For a buffered Python file object, the documented sequence is to call flush() first, then os.fsync(f.fileno()).
That means the two paths do not measure identical completion conditions: the buffered path measures the write call’s return, while the forced-sync path also waits for the requested sync operation. Whether an application needs that stronger guarantee depends on what it is trying to protect; the timing alone does not establish that every generated file should be synchronously flushed.
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How to time the whole workflow
For short elapsed-time measurements, Python’s time.perf_counter() is a high-resolution clock. Its value is meaningful as a difference between readings, not as a timestamp. It includes time spent sleeping, so a deliberate pause or a post-write wait will appear in the elapsed measurement.
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- Mark separate spans for connection/first byte, streamed generation through the last token, local decode and writes, and any post-write work such as formatter or watcher activity.
- Use the same clock consistently for start and end readings. Record elapsed differences for each span and for the end-to-end task.
- Label the completion condition of each write measurement: buffered return, or flush followed by forced sync.
- State what the run actually represents—a small fixture, a trusted real patch, or a production/load test—and avoid comparing unlike workloads as though they were equivalent.
This breakdown answers a more useful question than “Is the model slow?”: where did wall-clock time go after the request began, and what work remained after the final token?
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What the numbers do—and do not—say about model speed
Lin’s takeaway was: “The remote model was not my bottleneck today. The work after the last token was.” That is a diagnosis of the author’s observed run, not a finding that local apply work generally outweighs model generation. The experiment’s small fixture and stated limitations do not support a vendor ranking or a broad performance conclusion.
Before changing models or paying for more compute, inspect the measured spans in your own workflow. If local apply or post-write work dominates a representative task, investigate that work; if generation dominates, the evidence points elsewhere. Lin also offers a rough one-third/two-thirds split as a debugging prompt, explicitly a crude fence rather than a scientific threshold.
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Source and scope
The example and its disclosed limitations appear in Dakota Lin’s Dev.to article, published September 17, 2026. Lin says the piece was prepared as part of MonkeyCode product outreach and that the service’s free model access and server were used for contrast runs. That disclosure does not make the fixture timings an endorsement, performance validation, or pricing comparison.
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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 & 11The relevant Python behavior is documented in the Python 3.14.8 references for os.fsync and time.perf_counter.
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