Originally published on AI Tech Connect.
What you need to know Three questions decide everything. Can you hold the weights? Route to experts fast enough? Keep the GPUs busy? Fail one and the deployment is not slow, it is impossible. Total parameters set memory; active parameters set compute. It is why a model activating 41 billion parameters still demands terabytes. Quantisation is the admission ticket. Kimi K3's 2.8 trillion parameters are roughly 1.4 TB at MXFP4 against roughly 5.6 TB at 16-bit, both before KV cache. A QAT release is a different risk from one you compress yourself. K3 is quantisation-aware trained from the SFT stage; a bf16 model you quantise post hoc leaves the quality cost for you to find. MoE pushes you towards expert parallelism, and interconnect is the hidden constraint. All-to-all on every layer means…
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