Ollama Cloud Quota: DeepSeek V3 Burn Rate

A developer's benchmark reveals that Ollama Cloud's quota burn rate for DeepSeek V3 (Pro) is not tied to model intelligence or size, with some smaller models draining credits as fast as larger ones. The test, published on GitHub and detailed in a blog post, shows users cannot predict remaining time based on model name alone.

Ollama Cloud Quota: DeepSeek V3 Burn Rate DeepSeek V3 Pro just wiped out my entire 5-hour Ollama Cloud quota in one session, which makes zero sense if you assume quota is tied to model "intelligence" or size. I expected a cheaper burn rate for a more efficient model, but the reality is completely different. I ran a benchmark across the available models to figure out exactly how this quota is being calculated. The results prove there is no direct correlation between the model's capability and how fast it eats through your credits. Some "smaller" or supposedly more efficient models are draining the quota just as fast—if not faster—than the heavy hitters. For anyone trying to optimize their AI workflow or avoid hitting the ceiling mid-project, you can't rely on the model name to guess your remaining time. If you want to replicate the test or see the specific drain rates, the evaluation script is available here: https://github.com/Microck/ollama-quota-bench And the full breakdown of the findings is detailed here: https://blog.micr.dev/blog/i-made-my-first-benchmark Next GenAI Engineer Resume: Feedback Request → /en/threads/3325/ All Replies （3） C Happened to me last week. Wasted my whole credit balance in an hour. Total rip-off. 0 J Check if you're using long context windows; that usually spikes the burn rate way faster. 0 D Try lowering your temperature settings; I found it helps keep the output concise and saves credits. 0