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A model can finish generating quickly while the workflow still feels slow. To find out why, measure the whole task in separate spans: remote connection and first byte, streamed generation, local decoding and file writes, and any post-write work. Dakota Lin’s small Python experiment, published September 17, 2026, illustrates why those distinctions matter—and why a single timing is not a benchmark.
What Lin’s fsync experiment shows
Lin timed a tiny JSON fixture that wrote two files into a throwaway directory. The experiment compared a write path without a forced sync against one that flushed each buffered file and then called os.fsync(). In Lin’s one local fixture capture, the measured write span was 3.9 ms without forced sync and 41.6 ms with it; the corresponding total times were 25.2 ms and 62.7 ms. These are the author’s sample timings, not independently verified results or expected timings for another machine.
The key point is not that fsync always adds a particular delay. A buffered write can return before the operating system has forced the file’s data to disk. Adding a sync changes what the measured span includes. Python documents os.fsync(fd) as forcing a file descriptor’s write to disk; for a buffered Python file object, its documented sequence is to call flush() and then os.fsync(f.fileno()). See the Python 3.14.8 os documentation.
Time the workflow in separate spans
To answer “Is the model slow or is apply theatrical?”, keep distinct work distinct. A first byte or first token says when generation began to arrive, not when the requested change was applied and the workflow was ready.
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- Remote connection and first byte: time the wait before the response begins, separately from generation.
- Streamed generation: time from the first response content through the final token.
- Local decode and writes: measure turning output into files and applying it.
- Forced sync: record whether the write span includes flush and
fsync, rather than comparing it silently with a buffered write. - Post-write work: measure formatter, watcher, or other work that runs after files change.
Report each span and the end-to-end elapsed time. This makes it easier to tell whether the delay happened remotely, during local apply, or after the write. It also prevents first-token latency from standing in for task completion time.
Measure short durations with an elapsed-time clock
Python’s time.perf_counter() is a high-resolution clock intended for measuring short durations. Take readings around the operation and subtract them; only the difference between readings is meaningful. The clock includes time spent sleeping, which is useful when measuring elapsed wall time rather than processor time. See the Python 3.14.8 time documentation.
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import os
import time
start = time.perf_counter()
with open(path, "wb") as f:
f.write(data)
f.flush()
os.fsync(f.fileno())
elapsed_ms = (time.perf_counter() - start) * 1000
print(f"write_and_fsync: {elapsed_ms:.1f} ms")For a buffered-write comparison, measure the same operation without the flush-and-sync sequence, and label that path clearly. Keep the fixture, files, machine, and surrounding work the same if the goal is to compare the two paths. The code above illustrates the documented sync sequence; it does not turn a single run into a representative benchmark.
Use the measurements to choose what to investigate
If local apply and post-write spans dominate a task, investigate those steps before changing models or paying for more compute. If connection or streamed generation dominates, inspect the remote side instead. Lin suggests using “over two thirds” or “over one third” as a crude fence for prompting a closer look at post-generation work; those are personal debugging heuristics, not scientific thresholds.
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A useful timing report should say what kind of run produced it. A tiny fixture, a trusted real patch, and a production or load test answer different questions. Lin’s capture used a fixture and did not trace editor internals, remote GPU scheduling, prompt-cache warmth, or variation from other users on a free server. It had no production traffic and was not a benchmark-laboratory study. The article also discloses that it was prepared as part of MonkeyCode product outreach and that the author used its free model access and server for contrast runs; that disclosure is not performance validation or an endorsement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this example cannot establish
The sample does not establish a universal fsync penalty, prove that local disk is usually the bottleneck, or rank model vendors. It is one local capture, without controlled repeated trials, a representative workload study, or a cross-machine comparison. Its value is as a reminder to time the whole task and identify which work each number includes—not as a prediction of another system’s latency.
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