Bench 2xMI50 Qwen3.5 27b vs Gemma4 31B (vllm-gfx906-mobydick)
Inference engine used (vllm fork) : https://github.com/ai-infos/vllm-gfx906-mobydick/tree/main Huggingface Quants used: QuantTrio/Qwen3.5-27B-AWQ vs cyankiwi/gemma-4-31B-it-AWQ-4bit Relevant commands to run : docker run -it --name vllm-gfx906-mobydick -v ~/llm/models:/models --network host --device=/dev/kfd --device=/dev/dri --group-add video --group-add $(getent group render | cut -d: -f3) --ipc=host aiinfos/vllm-gfx906-mobydick:latest FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" OMP_NUM_THREADS=4 VLLM_LOGGING_LEVEL=DEBUG vllm serve \ /models/gemma-4-31B-it-AWQ-4bit \ --served-model-name gemma-4-31B-it-AWQ-4bit \ --dtype float16 \ --max-model-len auto \ --gpu-memory-utilization 0.95 \ --enable-auto-tool-choice \ --tool-call-parser gemma4 \ --reasoning-parser gemma4 \ --mm-processor-cache-gb 1
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Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
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Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
arXiv:2603.06928v2 Announce Type: replace Abstract: Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced per
qwen3.5 vs gemma4 vs cloud llms in python turtle
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