wifi-ruview/docs/adr/ADR-147-benchmark-proof.md

ADR-147 Benchmark Proof — OccWorld on RTX 5080

Date: 2026-05-29 Hardware: NVIDIA GeForce RTX 5080 (15.47 GB VRAM), CUDA 12.8 Model: OccWorld TransVQVAE (random weights — pre-domain-fine-tuning baseline) PyTorch: 2.10.0+cu128 mmengine: 0.10.7 Python env: /home/ruvultra/ml-env

Context

This document proves that the OccWorld TransVQVAE model builds, loads, and runs end-to-end on the local RTX 5080 at acceptable latency before any domain fine-tuning on RuView CSI/occupancy data. All numbers are measured from a cold Python process; no weights were loaded from a checkpoint (the config references out/occworld/epoch_125.pth which is absent — random initialisation is used throughout). Prediction quality numbers are therefore a baseline-without-domain-fine-tuning reading, not a target metric.

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1. Model Metrics

Metric	Value
Architecture	TransVQVAE (VAE-ResNet2D encoder/decoder + autoregressive transformer)
Total parameters	72.39 M
Trainable parameters	72.39 M
Weight initialisation	Random (no checkpoint — epoch_125.pth absent)
Model in-memory size	276.1 MB (float32)
Sub-module — VAE	14.17 M params
Sub-module — Transformer (PlanUAutoRegTransformer)	58.18 M params
Sub-module — PoseEncoder	0.02 M params
Sub-module — PoseDecoder	0.02 M params
Input tensor	(1, 16, 200, 200, 16) int64 — batch × frames × X × Y × Z
Input semantics	18-class occupancy labels (nuScenes schema); 17 = empty
Output — sem_pred	(1, 15, 200, 200, 16) int64 — 15 predicted future frames
Output — pose_decoded	(1, 3, 1, 2) float32 — 3-mode ego-motion predictions

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2. Inference Latency (batch=1, 10 runs, post-3-run warmup)

Metric	ms
Run 1 (cold JIT)	231.7
Run 2	227.6
Run 3	208.9
Run 4	208.8
Run 5	209.0
Run 6	208.7
Run 7	208.8
Run 8	208.7
Run 9	209.0
Run 10	208.9
Mean	213.0
P50	208.9
P90	228.0
P99	231.3
Min	208.7
Max	231.7
Throughput (15 frames predicted per inference)	70.4 predicted frames/sec
Per-frame latency	14.2 ms/predicted-frame

Notes:

• Runs 1–2 are ~22 ms slower than steady-state (CUDA kernel compilation).
• Steady-state (runs 3–10) is remarkably stable: 208.7–209.0 ms (0.2 ms jitter).
• The P99–mean spread of 18 ms is entirely from the first two JIT runs.

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3. VRAM Profile

Stage	GB (allocated)	Notes
Baseline (before model load)	0.000	Clean process, CUDA context not yet created
After model load (idle)	0.270	Weights resident, no activations
During inference (peak allocated)	3.368	Forward pass activations + VAE codebook lookup
After inference (retained)	2.095	KV-cache / activation buffers not freed
Peak reserved (PyTorch allocator)	6.543	PyTorch memory pool; returned to OS on empty_cache()
Total VRAM on device	15.47
Headroom at inference peak	12.10	Available for larger batches or multi-model co-location

VRAM budget analysis:

• Idle footprint (0.27 GB) is small enough to co-locate with a RuView CSI inference pipeline on the same GPU without contention.
• Peak inference (3.37 GB allocated / 6.54 GB reserved) leaves >9 GB free for a batched training run alongside real-time inference.

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4. Prediction Quality (Synthetic Linear Walk)

Setup: synthetic 200×200×16 occupancy grid; a single pedestrian (class 8) placed at voxel (100, 100, 8) and moved +2 voxels/frame eastward (≈1 m/s at nuScenes 0.5 m/voxel, 2 Hz). Fifteen past frames fed as context; 15 future frames compared against linear ground truth.

Metric	Value	Notes
Voxel resolution	0.5 m/voxel	nuScenes standard
Frame rate	2 Hz	0.5 s per frame
Person speed (ground truth)	1.0 m/s east	2 vox/frame
MDE — mean displacement error	18.98 vox / 9.49 m	averaged over 15 future frames
FDE — final displacement error	32.46 vox / 16.23 m	at frame 15 (7.5 s horizon)
Pedestrian voxels predicted (total, 15 frames)	1,604,019	model over-predicts occupancy with random weights

Frame-by-frame comparison (first 5 of 15):

Frame	GT centroid (X,Y)	Predicted centroid (X,Y)	Displacement (vox)
1	(102, 100)	(97.0, 96.3)	6.3
2	(104, 100)	(97.5, 97.1)	7.1
3	(106, 100)	(97.3, 96.6)	9.4
4	(108, 100)	(97.4, 97.2)	10.9
5	(110, 100)	(97.7, 96.2)	12.9

Interpretation: with random weights the transformer predicts a near-static pseudo-centroid biased toward grid centre rather than tracking the moving target. This is the expected behaviour of an uninitialised network and establishes the pre-training MDE baseline. After domain fine-tuning on annotated CSI-derived occupancy sequences the MDE target is ≤2.0 vox (≤1.0 m) at 5-frame horizon per ADR-147 §5.

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5. IPC Round-trip

The OccWorld server (configured port 25095) was not running during this benchmark session. IPC round-trip measurement was therefore skipped.

Port	Status
25095 (OccWorld config)	closed — server not running
8080 (other service)	open (unrelated)

To measure IPC latency: start the serving process configured in config/occworld.py (port = 25095), then re-run the benchmark. Expected IPC overhead is negligible (<1 ms localhost TCP) compared to the 213 ms inference latency.

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6. Verdict

PASS — all structural benchmarks pass.

Check	Result
Model builds from config without error	PASS
Model loads to CUDA in <500 ms	PASS — 281 ms
Forward pass completes without error	PASS
Steady-state latency ≤500 ms at batch=1	PASS — 208.7 ms (P50)
Peak VRAM ≤ 8 GB	PASS — 3.37 GB peak allocated
Output shape correct (1,15,200,200,16)	PASS
Pedestrian voxels present in output	PASS — 1.6 M voxels
Pre-training MDE documented	PASS — 18.98 vox baseline recorded
IPC test	SKIP — server not running

Summary: OccWorld TransVQVAE runs end-to-end on the RTX 5080 at 213 ms mean latency with a 3.37 GB VRAM peak. The model is ready for domain fine-tuning on RuView CSI-derived occupancy data. Prediction quality numbers (MDE 9.49 m) confirm that the random-weight baseline is far from target and that domain fine-tuning is a prerequisite before any deployment evaluation. The VRAM headroom (12.1 GB free at inference peak) is sufficient to run training and inference concurrently on the same device.