How GLM5.3 Sparse Attention Affects HBM Memory Usage GLM-5.3, KV Cache Offloading, HiSparse, AgentX TileRT, InferenceX DeepSeek Sparse Attention, IndexShare, Single-rollout Asynchronous Optimization, Cybersecurity

Built on the glm5_next architecture and optimized with ModelOpt, this model uses FP4 quantization to shrink weights dramatically. Safetensors format ensures safe, fast loading. It's a lean take on a powerful multimodal design.

Hermes x GLM5.2 x Pokémon i asked GLM5.2 to one-shot a playable Pokemon game via Hermes (using /goal) it generated everything, including the assets and the sprites. Cost: ~110k tokens / ~$0.5 The game visuals could be improved but the results are satisfying considering the speed and the cost. With more back and forth, an improved and more complete version could be reached very quickly.

Ornith 1.0 32B🦜 vs GLM5.2 🐲 i ran Ornith 1.0 32B locally on my Mac M2 Max (~60 tok/s) against GLM-5.2 on the same 5 animation tasks: - burning paper - paper slice - bubble wrap - zipper opening - mechanical watch as expected GLM-5.2 wins on quality, but Ornith is surprisingly competitive for a model running entirely on-device. the gap is smaller than I expected

BREAKING NEWS🚨: RUBIN NVL72 HITS 862 tok/s/user on GLM5.3 744B on agentic workloads! In less than 8 days, NVIDIA has increased interactivity on Rubin by close to 2x through low-latency specialized kernels.

Astra's opinion after evals of proposed cost+time-saving subagent candidates (Grok 4.6, GLM5.3-Flash, DeepSeeks, Luna, omen-alpha). Evals mostly based on recent projects, including this very brokerage plugin. Too easy, but interesting MVP. V4-Flash is still fast and good.