KVCache.AI(@KVCache_AI):A great read on how GLM-5.3 helped optimize its own inference stack, tripling end-to-end throughput in under two weeks, all driven by a dense feedback loop between the agent and the system. One particularly interesting case for us: while debugging a KV Transfer bottleneck, the team traced a >20% Prefill + KV Transfer performance gap due to Python GIL contention between DeepEP and the Mooncake Transfer thread. After releasing the GIL in the relevant C++ paths, the gap dropped to below 1%.

2026.09.17 09:25

A great read on how GLM-5.3 helped optimize its own inference stack, tripling end-to-end throughput in under two weeks, all driven by a dense feedback loop between the agent and the system. One particularly interesting case for us: while debugging a KV Transfer bottleneck, the team traced a >20% Prefill + KV Transfer performance gap due to Python GIL contention between DeepEP and the Mooncake Transfer thread. After releasing the GIL in the relevant C++ paths, the gap dropped to below 1%.

Z.ai@Zai_org

2026.09.17 07:05

We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone.