Forecast: Enterprise AI model deployment requires careful VRAM sizing for 2026

Enterprise teams planning private GPU clusters for 2026 MoE AI models often underestimate required memory. A common error is sizing for only active parameters per token instead of the full resident model and concurrent request caches. This can cause system failures or degraded performance when deploying models like DeepSeek-V4.1-Flash or Kimi K2.5. The article provides a detailed formula and reference table for calculating total VRAM needs. It also compares the costs of in-house clusters versus using commercial AI APIs.