Dense models versus MoE: distinguish total parameters from active parameters
Using the original Mixtral and Switch Transformer papers, examine how expert routing changes computation and how to compare memory needs and task results when self-hosting.
Choose an approach
MoE models select some experts for each input, whereas dense models do not use this sparse expert-selection mechanism. For self-hosting, check total and active parameters as well as how the backend places expert weights. Fewer active parameters do not mean the remaining weights can be ignored. Architecture names alone cannot determine which model fits your device.
Expert routing changes which components perform computation
The Mixtral paper describes selecting experts for each token and combining their outputs. Switch Transformer studies a sparse architecture with simplified routing. MoE models can use different expert counts and routing strategies; one paper’s configuration is not a universal rule.
Total parameters affect the weights that must be stored, while active parameters help describe computation during processing. Deployment may also move data between devices, so memory and computation cannot be represented by one number. Both dense and MoE models have specific sizes and versions; compare actual models rather than abstract categories.
Give both models the same practical inputs
Fix task requirements and input length, such as identical documents for classification and summarization, and check whether each model follows the requirements. Training data differences also affect results; not every difference comes from expert routing. Record complete model names and versions and retain failed examples separately.
Before loading, confirm that the target backend supports the architecture, then check peak memory with long contexts and concurrent requests. Record any offloading to other devices. Comparing one model entirely on a GPU with another that repeatedly moves weights does not represent equivalent operating conditions.
Include expert placement and recovery in maintenance
Self-hosting also requires maintaining format conversions, backend upgrades and weight storage. A small operation with one device should establish a sustainable configuration first. Sparse computation in a paper does not remove the need for capacity checks. No architecture is declared a universal winner here.
Evaluate task quality and complete request latency rather than citing only generated tokens per second. Save the current configuration and replay identical inputs after upgrades to detect loading or output regressions. This article checks mechanisms in the two original papers; it does not reproduce their training or measure self-hosted throughput.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.