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The paper presents a Pareto atlas of LLM inference optimizations, mapping cost, quality, and latency trade‑offs for Qwen2.5‑7B‑Instruct on L4, A100, and H100 GPUs. Using 54 measured configurations and a calibrated simulator, it identifies 18 of 36 setups on the Pareto frontier, showing that combined methods outperform single ones. Quality tests reveal that AWQ 4bit and FP8 weights offer significant latency reductions while largely preserving accuracy, but naive FP8 KV caching fails to answer any questions correctly.
arXiv:2607. 01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims require controlled evaluation.
arXiv:2607. 26238v1 Announce Type: cross Abstract: We investigate lightweight raptor-species classification for real-time edge deployment in wind-turbine collision mitigation.
FALCON is a camera‑based 3D occupancy front‑end built on the AMD Kria KR260 FPGA that provides explicit latency guarantees for autonomous driving perception. The system uses cycle‑bounded programmable‑logic compute, a measured inference envelope over 100k frames, and a watchdog to flag stale outputs, achieving all 300k proxy frames within a 100 ms deadline plus at most 10.2 ms. On the full FlashOcc pipeline the board attains 29.88 mIoU on the Occ3D‑nuScenes set, matching 92.8 % of an FP32 baseline, while maintaining tighter tail latency than competing Jetson Orin and datacenter GPUs.
arXiv:2610.34717v1 Announce Type: cross Abstract: Vision--language--action (VLA) models increasingly run on edge devices aboard robots, where inference latency constrains closed-loop robot control. H...
The paper evaluates Muon, a data‑efficient optimizer, against AdamW on a single A100 GPU across 17.7M‑ and 33.6M‑parameter causal language models and a 2.70M‑parameter vision transformer. Using a 23‑candidate development budget and five matched seeds, Muon consistently reduces validation‑loss area under the curve by 3.25–22.44% and lowers test loss in all 20 primary pairs, while AdamW outperforms Muon on language tasks by 3.38–6.96%. Muon’s full optimizer call consumes 56.1–69.6% of training time versus 6.1–9.1% for AdamW, and it shows higher expected regret under restricted candidate pools, indicating trade‑offs between data efficiency and computational cost for small models with reference kernels.