●builderYou can access MIT-licensed weights at zai-org/GLM-5.2, but budget for 1.51 TB storage and multi-GPU inference infrastructure before planning any deployment.
●researcherThe MoE sparsity ratio (~40B active of 753B total) and jump from 200K to 1M context are worth benchmarking against Mixtral and DeepSeek-V3 on long-context tasks.
●founderA permissively licensed model at the top of independent benchmarks shifts the cost calculus for self-hosted inference, but the hardware floor is still very high for most startups.