Unverified50% confidenceDecision RuleExact time
For a very large MoE model such as 816B, even if only 42B parameters are activated, the original weights still need to reside fully in memory, so quantization is almost a prerequisite for running it on consumer-grade hardware.
1
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
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Related Claims
UnverifiedMeta's LLaMA 2 70B model still requires approximately 35 GB of VRAM to fully load even with 4-bit quantization.72% similarUnverifiedWhen deploying Kolibri, the MoE model still requires loading all 78B parameters into VRAM, making VRAM capacity the primary constraint.72% similarUnverifiedEven if the model weights are open, self-hosting still requires a considerable scale of hardware investment.71% similarUnverifiedTiered caching, quantization compression, and computation-transfer overlap are the general optimization approaches for deploying very large MoE models on consumer-grade hardware.71% similar
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