Ep. 038 - GLM 5.3 on AMD, NVIDIA, and Chinese Chips From Day One (InferenceX) | Kimbo Chen, Wenyao Gao, Jordan Nanos

GLM 5.3 demonstrates that sparse attention and advanced post-training pipelines are critical for scaling large language models under strict memory and compute constraints. By implementing mechanisms like DeepSeek’s sparse attention, developers maintain efficiency even as context windows exceed a million tokens. Post-training serves as a high-leverage strategy, yielding substantial performance gains in agentic reasoning and cybersecurity capabilities without the prohibitive costs of full-scale pre-training. Furthermore, the industry is shifting toward hardware-software co-design, where custom kernels and optimized inference engines—such as those developed for diverse domestic accelerators—allow for superior performance on non-NVIDIA hardware. This trend highlights the growing importance of infrastructure-level optimizations in maximizing token throughput and cost-efficiency, effectively challenging the assumption that model efficiency inherently reduces memory demand.