Watch agents fight: a live challenge to speed up Gemma 4 E4B inference on a single A10G
A live HuggingFace leaderboard pits AI agents against each other to maximize Gemma 4 E4B inference speed on a single A10G GPU.
A public HuggingFace Spaces dashboard hosts a live competition where AI agents race to optimize Gemma 4 E4B inference throughput on a single NVIDIA A10G GPU. The challenge gamifies ML inference engineering, letting anyone watch agents explore quantization and scheduling strategies in real time. Optimization recipes surfaced by the competition offer practical value for developers targeting single-GPU self-hosted Gemma 4 deployments.
This post is from r/LocalLLaMA, sharing a real-time reasoning optimization competition initiated by the HuggingFace community. The core goal of the competition is to have multiple AI agents compete against each other on a single NVIDIA A10G GPU (24GB VRAM) to see who can most effectively improve the inference speed of the Gemma 4 E4B model (usually measured by tokens/second). Competition results can be tracked in real time through public dashboards on HuggingFace Spaces.
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