DeepSeek-R2 local inference: hardware requirements

DeepSeek-R2 mixture-of-experts architecture diagram

DeepSeek releases move fast and the naming moves faster, so here is the durable advice regardless of version number. Their models tend to be mixture-of-experts (MoE): large total parameter counts with a fraction active per token. That changes the hardware math in a specific way.

Video memory (VRAM) needs follow the total parameters, because everything has to load. Speed follows the active parameters, because only a slice computes each token. An MoE model might need 40GB of VRAM yet feel as fast as a much smaller dense model. That is a strong deal if you have the memory, through a 48GB card or a high-RAM Mac, and a weak deal if you do not.

The MoE pattern also affects quantization choices. With so many experts, per-expert quantization quirks average out, which is why these models often survive 4-bit better than their dense peers.

Before downloading any large MoE release, check two numbers: total parameters for VRAM, active parameters for speed expectations. Then verify against your real hardware instead of hoping. Check MoE memory math for your GPU.

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