Cloudflare Details VRAM Optimization for Large-Scale Kimi and GLM Deployment

Cloudflare shared engineering insights on deploying trillion-parameter models like Kimi K2.6 and GLM 5.2 on its Workers AI platform. By applying FP8 and INT4 quantization to the KV cache, which depletes VRAM faster than model weights, Cloudflare successfully doubled concurrency for long-context MoE models.

2026-08-03 ~ 2026-08-05 · 4 related posts

• Cloudflare Details Inference Optimizations for Running Kimi and GLM at Scale — michellechen · 2026-08-03
• Cloudflare's LLM Serving Math: FP8 and INT4 Quantization Double Concurrency — arpit_bhayani · 2026-08-05

2 near-duplicate retellings: michellechen · arpit_bhayani