Smaller, faster, safer: running Kimi and GLM at scale
Source
Cloudflare
Author
Cloudflare
Published
Why it matters
If you're serving large open-weight models like Kimi or GLM yourself, this breaks down practical levers — KV cache quantization and weight compression — for cutting GPU memory pressure without silently degrading output quality.
A Cloudflare engineering blog post detailing techniques used to serve large open models like Kimi and GLM more efficiently on Workers AI, including quantizing KV caches and compressing model weights to reduce GPU memory pressure, plus added integrity checks to ensure outputs remain trustworthy after compression.
Transcript
Serving frontier models like Kimi and GLM means fighting for GPU memory. Here's how we quantize KV caches, compress model weights, and add integrity checks to serve them faster, cheaper, and safely. https://t.co/bQRkjuNx2j
