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Colibri explained: streaming MoE experts across storage, RAM and VRAM
Understand why a model can fit without promising that it will generate quickly on your hardware.
Browse all 9 articles in this open-source series. Understand why a model can fit without promising that it will generate quickly on your hardware.
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Understand why a model can fit without promising that it will generate quickly on your hardware.
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Understand why a model can fit without promising that it will generate quickly on your hardware.
Use the documented source path without accidentally starting a large model download.
Inventory runtime dependencies and recovery state before exposing inference as a service.
Separate memory-tier policy, inference computation and the Python control surface.
Diagnose packaging and launcher-path failures before inspecting inference kernels.
Use the upstream protocol to avoid mistaking page-cache speed for model performance.
Keep downloaded artifacts, network access and resource budgets within an explicit boundary.
Compare capacity, latency, quality and ownership against your actual workload.
Create a reproducibility ledger before attempting another inference optimization.