| ---
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| license: apache-2.0
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| task_categories:
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| - other
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| tags:
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| - benchmark
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| - inference
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| - cold-start
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| - llm-serving
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| - mlops
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| - systems
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| size_categories:
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| - n<1K
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| configs:
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| - config_name: default
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| data_files: observations.csv
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| ---
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|
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| # LLM Container Cold-Start Benchmark
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|
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| Measurements of how long it takes to bring a language model from cold storage to
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| a state where it can serve its first token, across **25 open-weight
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| models** spanning **17 architecture families** and
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| **100.9 GiB** of checkpoints, on a single NVIDIA T4.
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|
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| Cold start is the latency a serverless or scale-to-zero inference platform pays
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| when it has no warm replica. It decomposes into weight transfer from storage,
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| deserialization into host memory, transfer to the accelerator, and engine
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| bring-up. This dataset measures each phase separately rather than reporting one
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| aggregate number, because the dominant term shifts with model size and setup --
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| and optimising the wrong term buys nothing.
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|
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| ## Headline finding: cold start is superlinear in checkpoint size
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|
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| Fitting `load_ms = a x GiB^b` gives **b = 1.58 to 2.10** across conditions, where b = 1 would mean constant throughput. **Doubling the checkpoint more than doubles the load time**, so no single MiB/s figure describes the system -- effective throughput is a function of model size.
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| Concretely, throughput holds near **1535 MiB/s** below **5.18 GiB** and falls to **758 MiB/s** above it -- a **2.02x** collapse.
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|
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| Throughput in the healthy regime: **1354 MiB/s (R2=0.868)**.
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| The most likely cause is **host memory, not the storage device**: the runtime
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| had roughly 12-13 GiB of usable system RAM, and the memory-mapped load path
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| depends on the OS page cache holding the checkpoint while tensors are
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| materialised. This is stated as a hypothesis with an obvious test attached
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| (re-run on a high-RAM instance and see whether the boundary moves), not as a
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| settled conclusion.
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| **Why it matters:** a cold-start budget extrapolated from small models will be
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| optimistic for large ones, and the gap widens with size. Below the boundary,
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| storage bandwidth is the lever; above it, more storage bandwidth does not help,
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| because the bottleneck has moved.
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|
|
| ## Other results
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|
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| - **safetensors vs legacy pickle:** median **1.71x** faster to
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| load over 20 models with identical weights. The format costs no
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| model quality and no hardware to change.
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| - **Storage tier:** a bandwidth-capped tier is roughly an order of magnitude
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| slower than local NVMe on the same checkpoints.
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| - **Engine bring-up** is a comparable cost to weight transfer at these sizes,
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| which is precisely the distinction that determines whether snapshot/restore
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| techniques would pay off for a given deployment.
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|
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| ## Files
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|
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| | File | Contents |
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| |---|---|
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| | `observations.csv` | One row per (model, experiment, condition), joined to checkpoint size, parameter count, shard count and family. |
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| | `raw/runs.json` | The raw merged ledger, unmodified, so all statistics can be recomputed from source. |
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| | `cross_model_report.md` | The generated analysis: scaling fits, per-model tables, projections, noise profile, limitations. |
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|
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| ## Columns
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| `model_key`, `repo_id`, `family`, `tier`, `params_b`, `checkpoint_gib`,
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| `n_shards`, `bytes_per_param`, `experiment`, `condition`, `p50_ms`, `p95_ms`,
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| `stdev_ms`, `rsd`, `n_trials`, `throughput_mib_s`, `gpu`, `device_class`,
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| `reliable`.
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| `reliable` is `False` where relative standard deviation is 30% or more. Those
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| rows are published rather than dropped -- a reader may reasonably pick a
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| different threshold -- but no conclusion above rests on them.
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|
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| ## Method
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|
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| - Each condition is repeated, warm-up runs are discarded, and **p50/p95** are
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| reported rather than a mean, because tail latency is what a scale-to-zero SLA
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| is written against.
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| - The OS page cache is evicted between trials (`posix_fadvise(DONTNEED)` on an
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| unprivileged runtime), so reads are genuinely cold rather than served from RAM.
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| - Runs were distributed across several Colab sessions coordinated through a
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| shared Postgres ledger with atomic claims and epoch fencing, so no task was
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| ever executed twice. Duplicate execution would not merely waste time -- two
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| workers would contend for the same disk and corrupt the I/O measurement.
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| - Registry metadata (checkpoint sizes, shard counts, gating) was read from the
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| Hugging Face API rather than estimated.
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|
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| ## Limitations
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|
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| - **Free-tier hardware.** A single consumer GPU with shared, contended host I/O.
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| The *ratios* and the *scaling behaviour* transfer; the absolute milliseconds
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| are not production numbers.
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| - **Noise.** Median RSD is around 15%, with a long tail concentrated in engine
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| bring-up where JIT compilation varies run to run. Small effects here are not
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| findings.
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| - **Not measured:** driver-level checkpoint/restore (proprietary GPU memory
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| snapshotting), multi-GPU tensor-parallel loading, and real network-attached
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| storage -- the remote tier is a software bandwidth cap, which bounds the
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| penalty from below rather than reproducing it.
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| - **Coverage gaps** are listed at the top of the report.
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|
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| ## Reproducing
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| - **Code:** https://github.com/priyansh-saxena1/coldstart-lab
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|
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| ```bash
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| pip install -e ".[distributed,plots]"
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| coldstart-fleet init --tier small --device-class t4
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| coldstart-fleet work --device cuda --device-class t4
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| coldstart-fleet merge --out ./merged
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| ```
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|
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| ## Citation
|
|
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| ```bibtex
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| @misc{coldstart_bench_2026,
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| title = {LLM Container Cold-Start Benchmark},
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| year = {2026},
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| note = {Dataset: https://huggingface.co/datasets/ArchCoder/llm-cold-start-benchmark}
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| }
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| ```
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|
|
| ## License
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|
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| Apache-2.0. Measurements only; no model weights are redistributed.
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|
|
