Ollama's Chinese Model Support Is Real — But Running Kimi and DeepSeek Locally Has a Hidden Cost

Short summary

Ollama now supports Chinese models (Kimi, DeepSeek, GLM-5), enabling local deployment without data leaving your infrastructure—but the hidden costs are significant. Quantization degrades 70B model quality noticeably, English documentation lags 6-12 months behind Chinese releases, and you'll need expensive hardware ($8,000+) to match cloud API performance. Local deployment makes sense only for specific compliance-driven or high-volume inference workloads; for most Western teams, the economics don't justify the infrastructure investment.

• •Quantization on 70B Chinese models requires Q5/FP16, not the efficient 4-bit quantization that works for smaller Western models
• •English documentation lags Chinese releases by 6-12 months; prompt engineering patterns differ significantly from Western models
• •True cost at scale: $8,000+ hardware investment performs 15-20% worse than hosted APIs on complex reasoning tasks
• •Local deployment is viable only for compliance-driven or high-volume (>10M tokens/day) inference; not a general-purpose replacement

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