Datasets:

witcheer
/

rtx-5090-benchmarks

Rig: one RTX 5090 32GB · transformers 4.57.1 (bf16, SDPA fallback, no MagiAttention/flash-attn) · greedy, temp 0 Model: nvidia/LocateAnything-3B (NVIDIA non-commercial license, 3.83B on disk: MoonViT-SO-400M + Qwen2.5-3B + untied head) — one generalist grounding model for open-vocab detection, referring expressions, pointing, GUI/scene-text/document grounding. Novelty: Parallel Box Decoding (PBD) — boxes decode as atomic 6-token blocks in one parallel step instead of token-by-token.

1. PBD verified: ~2.1x, and it survives the SDPA fallback

The claimed "up to 2.5x" over AR decoding was measured by NVIDIA with their optimized stack. On consumer Blackwell (sm_120, CUDA 12.8) neither MagiAttention (requires CUDA ≥ 13) nor flash-attn is available, so the model runs its SDPA fallback — and PBD still delivers:

generation_mode	tok/s	forward steps	boxes/s (model stat)
slow (AR)	99.9	56	14.3
fast (PBD)	207.3	16	31.3
hybrid	207.2	16	31.3

2.07x token throughput, 2.19x boxes/sec on an 8-box detection prompt, deterministic at temp 0. Inference footprint on normal images: 7.75 GB — it ran beside a live 27B llama-server. Two quality notes: fast emitted one fewer box than slow on the same greedy prompt (speed is not entirely free), and single-instance grounding cannot abstain — ask for "the leftmost animal" on an animal-free street and it returns a confident whole-image box. Detection mode's explicit <box>None</box> negatives, by contrast, are clean.

2. The wall: the official protocol cannot run on consumer GPUs

The shipped processor allows 25,600 ViT patches per image (native-res screenshots). Without flash-attn, MoonViT's SDPA fallback materializes full attention — on a 6016x3384 screenshot that is a single 40.6 GB allocation. An H100 80GB absorbs it; no consumer card can. The largest in_token_limit that fits 32 GB is 12288 (peak 29.5-29.8 GB, GPU otherwise empty), which the processor satisfies by extra-downscaling big screenshots. Every consumer-GPU number for this model is therefore measured under forced downscaling — ours included, below.

3. ScreenSpot-Pro: 55.3% measured vs 60.3 claimed — and where it actually breaks

Full benchmark, 1,581 instructions, official pointing prompt (Point to: {instruction}.), point-in-box metric, greedy, hybrid mode, 2.9 s/item (75.6 min total), zero OOMs.

cut	acc	n
overall	55.3%	1581
paper claim (H100, native res)	60.3	—
text targets	63.2%	977
icon targets	42.7%	604
1440p-2.5K screenshots	58.0%	849
4K screenshots	53.3%	632
above 4K	48.1%	81

The claim is plausible, not refuted. Our deviation (12288 vs 25600 patches) only pushes accuracy down, and the resolution gradient (58.0 → 53.3 → 48.1) is consistent with downscale severity — though bigger screenshots may also simply be denser UIs; the two can't be fully disentangled from this run. The honest consumer-card number for this model on ScreenSpot-Pro is ~55%.

The real fault line is text vs icon (63.2 vs 42.7). It reads textual targets well and struggles with abstract iconography — the per-app table says the same: Word 82.1%, EViews 80.0%, Unreal 77.1% at the top; AutoCAD 26.5%, FruityLoops 38.6% at the bottom (icon-dense, idiosyncratic UIs).

Honest caveats

in_token_limit 25600 → 12288 is a forced protocol deviation (32 GB ceiling); our 55.3 understates what the model does at native resolution.
Greedy decoding (their reference config samples at temp 0.7); deterministic but not their exact recipe.
ScreenSpot-Pro metric here is point-in-target-box on the English instruction, one attempt, no retries.
The model is non-commercial (research/evaluation only) — relevant before anyone builds on it.
PBD speed was measured on one detection prompt shape; the 2.07x is workload-typical, not a sweep.

Reproduce

scripts/la_probe.py (smoke / PBD speed A/B) · scripts/la_oomtest.py (the in_token_limit wall) · scripts/la_screenspot.py --token-limit 12288 (full eval) · raw per-item results: raw/la-screenspot.jsonl (this dataset).