Reference library
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Paper summary
DeepSeek-V2 combines sparse mixture-of-experts computation with latent compression of attention's key-value cache. The report evaluates training economy and inference throughput while describing pretraining and post-training.
Relevance to the project
An engineering comparison for separating parameter storage from active computation and compact communication. Optional LLM use would require hardware-specific measurements.
A large language-model architecture is not a tested design for neuron-local F.
Source record
- Reference ID
- deepseek-v2
- Archived version
- 2405.04434v5
- Summary provenance
- reference-library/papers/deepseek-v2/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.