[ShaiShmuel]

Problem

On an NVIDIA L4, one LightOnOCR-3-4B page can run the SGLang engine out of memory at mem_fraction_static: 0.85. It fails in the vision encoder on the first prefill (get_image_feature). The engine exits, so every page in flight fails.

A request with default settings reaches this. A 3520×3520 page (12.4 MP, 12,100 visual tokens) with the default 12,288 new tokens fits the 24,576-token window and still ran out of memory. The processor does not resize a page under its 16,777,216-pixel limit, and examples/photo-transcription sends images unchanged. Xiaomi-OCR-0 hit the same failure in #875. deploy/helm/sie-cluster/values-aws.yaml runs this bundle on an L4 pool.

Change

• Lower mem_fraction_static from 0.85 to 0.70 in the default and markdown profiles. SGLang keeps (1 - fraction) of the memory free at launch as runtime headroom.
• Record the measurement in the catalog comment.
• Pin 0.70 in test_lightonocr3_profiles.py, and remove this model from the 0.85 list in test_sglang_memory_profiles.py.

Measurements

NVIDIA L4 (23,034 MiB), with 22,589 MiB free at launch. Each row is the catalog profile with only mem_fraction_static changed. Peak is nvidia-smi sampled every 0.25 s.

Fraction	Pages	Output limit	Result	Used after load	Peak (free at peak)	KV token capacity	SGLang running limit
0.85	one 3520×3520	12,288 (default)	OOM after 1.0 s, engine exited	20,298 MiB	n/a	169,018	96
0.85	one 4096×4096	8,064	OOM after 1.1 s, engine exited	20,602 MiB	n/a	169,018	96
0.75	one 4096×4096	128	OOM after 1.8 s, engine exited	18,082 MiB	n/a	131,081	75
0.70	one 4096×4096	8,064	Pass	16,946 MiB	21,859 MiB (733)	112,898	64
0.70	24 mixed sizes, 0.8-16.8 MP, 8 at a time	128	24 of 24 pass	16,946 MiB	21,863 MiB (729)	112,898	64
0.70	32 distinct 4096×4096 at once	8,064	No OOM, 17 finished, 15 hit the client's 1,500 s read timeout	16,946 MiB	21,865 MiB (727)	112,898	64

The engine stayed up with the same process throughout every 0.70 run. The 15 unfinished pages were a client limit, not a server error, so those 32 pages show no OOM rather than 32 completions.

A full-size page peaks 4.8 GiB above the memory in use after load. The engine reported 16,398 input tokens for a 4096×4096 page, which is 16,384 visual tokens plus the prompt.

GLM-OCR shares the 0.85 setting and is unchanged, because it passed the same test. Its processor counts the temporal factor against longest_edge, so its real limit is 4,816,896 pixels (6,144 visual tokens). At 1792×2688, one page peaked at 20,181 MiB. 32 distinct pages all completed, with a peak of 20,383 MiB and 2.2 GiB free.

Impact

• KV pool: on an L4, token capacity falls 33% (169,018 to 112,898) and SGLang's running limit falls from 96 to 64. The KV pool sets the batch, not the running limit: on 2048 px pages, about 40 pages run at 0.85 and 26 at 0.70. At 64 in flight, throughput falls 11% and median latency rises 16% (measurements).
• Larger GPUs: the fraction applies to every GPU size. Above 24 GB this reserves more headroom than a page needs and shrinks the KV pool with it. On a 96 GB RTX PRO 6000, capacity falls 20% (1,225,927 to 983,393) with no measurable throughput cost on 2048 px pages at 64 in flight. Full-size pages were not measured there, and by arithmetic should queue sooner at 0.70.
• Margin: 0.7 GiB free at the peak is enough for a dedicated L4, but not for another model sharing that GPU.
• Unchanged: a 16 MP page with the default 12,288 new tokens still exceeds the window (16,398 + 12,288 = 28,686) and is rejected before the encoder. It returns HTTP 500 today.

Validation

GPU runs used commit 8563ef3 with sglang 0.5.10.post1, transformers 5.3.0, torch 2.9.1 and flashinfer-python 0.6.7.post3. One difference from the sglang-vision-extract bundle lock: the test image had flash-attn 2.7.4, not flash-attn-4 4.0.0b19.

python -m sie_server.cli serve --port 8080 --device cuda \
  --models lightonai/LightOnOCR-3-4B --preload lightonai/LightOnOCR-3-4B --models-dir <dir>
SIEClient(...).extract(model, {"id": ..., "images": [page]}, options={"max_new_tokens": ...}, max_oom_retries=0)

Unit tests on this branch:

pytest packages/sie_server/tests/config packages/sie_server/tests/adapters/test_sglang_memory.py
# 1725 passed, 532 skipped
pytest packages/sie_server/tests/adapters/test_sglang_vision_extract.py packages/sie_server/tests/adapters/test_lighton_ocr3.py \
  packages/sie_server/tests/config/test_lightonocr3_profiles.py packages/sie_server/tests/config/test_sglang_memory_profiles.py \
  packages/sie_server/tests/adapters/test_sglang_memory.py
# 161 passed

The new test fails if either profile returns to 0.85 (checked by changing the markdown profile back).

🤖 Generated with Claude Code

Summary by CodeRabbit

• Improvements
  • Adjusted memory settings for the default and Markdown OCR profiles to help manage memory use when processing large pages.
  • Clarified the concurrency and memory guidance for L4 deployments.
• Tests
  • Added checks to verify the OCR profile settings are applied when profiles load.

[coderabbitai]

Walkthrough

The LightOnOCR default and markdown profiles lower mem_fraction_static to 0.70. Profile tests check the memory fraction and concurrency setting. The specialized-profile calibration test no longer includes LightOnOCR.

Changes: LightOnOCR profile calibration

Layer / File(s)	Summary
Update profile settings and validation packages/sie_server/models/lightonai__LightOnOCR-3-4B.yaml, packages/sie_server/tests/config/test_lightonocr3_profiles.py, packages/sie_server/tests/config/test_sglang_memory_profiles.py	The default and markdown profiles set mem_fraction_static to 0.70. Comments document L4 concurrency and memory observations. Tests assert the memory fraction and max_concurrent_requests of 128, and remove LightOnOCR from the specialized-profile calibration test.

Suggested reviewers: svonava, krisztian-gajdar

Severity of issue fixed: Medium

Merge Risk: Minimal · up to 4b7b6

No concrete merge-blocking risk is established by the available evidence.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

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Title check		The title names the main change: lowering SGLang mem_fraction_static from 0.85 to 0.70 for the LightOnOCR-3-4B profiles so full-size pages fit in GPU memory. The changeset edits the LightOnOCR-3-4B YA…
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Explanation

The title names the main change: lowering SGLang mem_fraction_static from 0.85 to 0.70 for the LightOnOCR-3-4B profiles so full-size pages fit in GPU memory. The changeset edits the LightOnOCR-3-4B YAML profiles, adds a profile test, and removes that model from a calibration test, which matches the title's scope. The title is short, single-sentence, and specific, and it uses the conventional fix(server) prefix.

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Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

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[coderabbitai]

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• packages/sie_server/models/lightonai__LightOnOCR-3-4B.yaml
• packages/sie_server/tests/config/test_lightonocr3_profiles.py
• packages/sie_server/tests/config/test_sglang_memory_profiles.py

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[ShaiShmuel]

Cost of 0.70 on normal-size pages

This fills the throughput gap in the Impact section. On an L4, 0.70 costs about 11% of throughput on normal pages. On a 96 GB RTX PRO 6000, it costs nothing measurable.

Method. The catalog default profile served through sie-server, changing only mem_fraction_static in a temporary copy of the YAML. Each row is 305 requests sent with SIEClient from the same container. The pages are 61 public document pages rendered at 400 DPI and downscaled to a 2048 px longest edge: 37 from the PDFs in examples/ and 24 from the superlinked/sie-task-evidence dataset. Each request changes one pixel, so no image repeats, and every run sends the same files. Pages average 3,090 input tokens (computed with the pinned processor) and 825 output tokens (counted with the pinned tokenizer). Each GPU ran 0.85, 0.70, 0.85, 0.70 in one container, with 8 CPUs and 48 GiB of memory. Every request returned without error, with no OOM and no engine restart. One page in the set appears to be blank: 5 of every 305 responses are empty, at both fractions.

Rows are in run order. Pages/s is 305 divided by wall time, so it includes the start and end of each run. Pages running is the mean of the engine's num_running_reqs from /v1/loads, sampled every 2 s during the steady part of the run.

NVIDIA L4 (23,034 MiB)

Fraction	In flight	Pages/s	Output tokens/s	Latency p50	Latency max	KV token capacity	SGLang running limit	Pages running	Peak GPU memory (free at peak)
0.85	64	0.322	266	163 s	502 s	168,977	96	40	22,257 MiB (336)
0.85	128	0.320	265	324 s	680 s	168,977	96	40	22,257 MiB (336)
0.70	64	0.287	237	192 s	471 s	112,865	64	26	18,903 MiB (3,690)
0.70	128	0.288	239	384 s	671 s	112,865	64	26	18,903 MiB (3,690)
0.85	64	0.323	267	163 s	518 s	168,977	96	40	22,539 MiB (54)
0.70	64	0.287	237	187 s	473 s	112,865	64	26	18,899 MiB (3,694)

RTX PRO 6000 (97,887 MiB)

Fraction	In flight	Pages/s	Output tokens/s	Latency p50	Latency max	KV token capacity	SGLang running limit	Pages running	Peak GPU memory (free at peak)
0.85	64	1.574	1,299	30 s	161 s	1,225,927	701	63	88,319 MiB (8,972)
0.85	128	1.820	1,506	56 s	159 s	1,225,927	701	126	88,337 MiB (8,954)
0.70	64	1.806	1,491	27 s	136 s	983,393	562	63	73,991 MiB (23,300)
0.70	128	1.935	1,592	52 s	149 s	983,393	562	127	74,009 MiB (23,282)
0.85	64	1.866	1,540	27 s	131 s	1,225,927	701	63	88,415 MiB (8,876)
0.70	64	1.872	1,543	27 s	131 s	983,393	562	63	73,991 MiB (23,300)

What this shows

• L4 cost: at 64 in flight, throughput falls 11% and median latency rises 16%. Repeat runs at each fraction agree within 0.3%.
• The KV pool limits the L4, not the running limit. Neither 96 nor 64 is reached: 40 pages run at 0.85 and 26 at 0.70, whether 64 or 128 are in flight. The rest wait, so 128 in flight adds latency and no throughput.
• 0.85 is tight on an L4 even on normal pages. Free memory at the peak was 336 MiB in one load and 54 MiB in another, with no OOM. 0.70 keeps 3.6 GiB free.
• RTX PRO 6000: no measurable cost. The last pair of runs took 163.4 s at 0.85 and 162.9 s at 0.70. About 63 of the 64 pages run at both fractions, with almost none waiting, using 19% of KV capacity at 0.85 and 23% at 0.70. 0.70 leaves 23 GiB free at the peak, against 9 GiB.
• One unexplained run: the first RTX PRO 6000 run was 16% slower than the last 0.85 run. I did not establish why, so the comparison above uses the later pair. The 128 rows have one run each, and the 0.85 one comes from that same first load, so they show no cost but do not measure a gain.

Not measured

Full-size pages on the RTX PRO 6000. By arithmetic, 64 pages at 16,398 input tokens need 1,049,472 KV tokens before any output. That is above the 983,393 available at 0.70 and below the 1,225,927 at 0.85, so a run of only full-size pages should queue sooner at 0.70.

Versions

Public main at 991540b with sglang 0.5.10.post1, transformers 5.3.0, torch 2.9.1 and flashinfer-python 0.6.7.post3. The same difference from the bundle lock applies as in the Validation section: flash-attn 2.7.4, not flash-attn-4 4.0.0b19.