A Quantized 2B Vision Model Outperformed 8B on a Quest 3 Test

A new Hugging Face Blog community article tests NVIDIA’s Cosmos-Reason2 vision-language models on a Meta Quest 3, asking a practical question for edge-AI builders: what does compression actually cost—and sometimes save?

The study evaluated roughly 10,000 inferences using Meta’s Aria Digital Twin data, with models running through llama.cpp. On the benchmark’s object-pointing task, Cosmos-Reason2-2B at Q8_0 delivered the strongest results: 56% within 50 pixels, 66% within 100 pixels, and a 35.1-pixel median error. Its 2.78 GB footprint also made it the only model among the top two configurations small enough for the headset.

That result beat the larger Cosmos-Reason2-8B at Q4_K_M, which occupied 5.76 GB and reached 52%, 63%, and 68% at the same thresholds, with a 40.5-pixel median error. The 2B Q4_K_M version was smaller at 1.95 GB but fell to 49% at 50 pixels and 61% at 100 pixels. The Qwen3-VL-2B control scored 42% and 61%, respectively.

Why it matters

For developers targeting standalone XR devices, the findings challenge the assumption that more parameters automatically produce better local inference. Higher-precision weights preserved enough accuracy to outperform a larger, more aggressively compressed model—while remaining deployable on Quest 3 hardware.

The gains were most visible at precise localization. At a relaxed 150-pixel threshold, results clustered between 66% and 71%, suggesting post-training and scale mainly improve where models point, rather than whether they detect an object at all. The researchers also report statistically significant improvements over the base model on both real and synthetic imagery, while cautioning that this custom benchmark is not a general leaderboard.

Source: Hugging Face Blog