Smaller, faster, safer: running Kimi and GLM at scale

Workers AI leverages Cloudflare data centers to run inference for sophisticated open models like Moonshot's Kimi K-series and Z.ai's GLM, facing challenges due to their memory demands. To manage this, techniques such as quantizing the KV cache and compressing model weights are employed, which allow more efficient memory usage without compromising model accuracy. By storing the attention keys and values in lower precision and reducing model weights from 8-bit to 4-bit, significant reductions in memory footprint are achieved, enabling support for more concurrent requests and lowering operational costs. These optimizations are validated using SGLang, an open-source inference framework, ensuring the improvements are shared with the community. Additionally, an integrity checking system is implemented to safeguard shared memory caches, maintaining reliability with minimal performance impact. These strategies aim to enhance service capacity and cost efficiency while maintaining the quality of the models, reflecting an ongoing effort to optimize the deployment of high-demand AI models.

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