From first principles, 27b dense vs 13b moe, means you spend ~2x more memory bandwidth per request amortized over the whole server (ie, you assume all experts are being concurrently used by some user during the forward pass, then on average the bandwidth required for one forward pass for any individual request is just the size of one expert). deepseek also have some innovations around kv cache and compressed attention which allow for further reductions in memory bandwidth which means that the thing that’s actually bottlenecking inference, (memory bandwidth) is significantly lower than for qwen 3.8, which has been optimized for running 1 instance ~= 1 user.

DeepSeek needs more RAM for weights, Qwen requires more compute.

Also, DeepSeek's KV cache requires less RAM than Qwen's. In concurrent situations (on servers) you load model weights once, but you have different context in each parallel session. So, it can also need less RAM than Qwen to serve, even if it's a larger model.

> and it needs 10 times more RAM.

More like 3-6.

Qwen 27B full quality is FP16. So 54GB. In practice most inference providers would serve FP8, so 27GB.

DeepSeek V4 Flash in full quality is mostly FP4. ~167GB official release.

So Deepseek has 140GB model size overhead... which is shared between 100s of users single inference node serves, so not even a gigabyte of VRAM per user.

Memory required for 200K of context per user:

  V4 Flash: 1GB. 
  Qwen 27B: 13GB.