💥New Release: Awex, a high-performance #RL training-inference weight synchronization framework. #opensource
🚀 It cracks the tough nut of training/inference weight params latency, syncing 1T-param models across thousands of GPUs in less than 6 seconds!
Join Awex https://t.co/gwotBgBIk6 and check out these amazing features!⬇️
⚡ Blazing Sync: Trillion-param models synced across 1000-GPU clusters in 6s, top-tier performance.
🔄 Unified Adapt: Auto-manage parallelism & tensor format/layout diffs, multi-model compatible.
💾 Zero-Redundancy: Transfers only needed shards, in-place updates, no realloc & copy hassle.
🚀 Multi-Mode: NCCL, RDMA, shared memory modes, max out NVLink/NVSwitch/RDMA, cut latency.
🔌 Heterogeneous: Works in co-located/separated, sync/async RL, RDMA for dynamic scaling.
🧩 Pluggable: Custom weight sharing, layout, integrate new train/inference engines.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Weight transfer between trainer and rollout engine is a real stall in large RL loops. Awex reports six-second syncs at trillion scale and works in co-located or disaggregated, sync or async setups.
