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AI Model Optimization
Gigabee currently runs five active inference models: Bee Nano for lightweight tasks at 5 credits, Bee Hover as the balanced general-purpose model at 10 credits, Bee Glide for high-quality output at 15 credits, Bee Code for programming tasks at 20 credits, and Bee Echo for text-to-speech at 5 credits per request. Image generation is also available at 20 credits. For users without credits, the system falls back automatically to free-tier models via OpenRouter so no request goes unanswered.
The models themselves perform well. The gap is in dispatch logic. Context-length is not checked before a job is assigned, meaning a long conversation can land on a GPU that would time out serving it. There is no automatic tier downgrade when a primary model is overloaded, and cold GPUs receive jobs at the same probability as warm ones the only signal used today is quality score, not readiness state.
Planned improvements include a context-length pre-check before dispatch, a fallback chain within each tier so saturation at the primary doesn't stall the queue, and a cold-start penalty that reduces routing probability for GPUs that have been idle for over 10 minutes unless they pass a warm-up probe. These changes don't touch the models they make the existing five models land more reliably under all conditions. solana:7NcMKMrXPBVCWPcs9SSqnF6ZGy5neAtzTZpFqZqLquaP