Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures research paper by DeepSeek, 2025
DeepSeek · May 14, 2025 · Inference and efficiency · 107 citations · 77 upvotes · unverified
What it shows
DeepSeek-V3 addresses hardware limitations through MLA, MoE, FP8 training, and Multi-Plane Network Topology, enabling efficient large-scale LLM training and inference.
UnverifiedHugging Face's summary; not yet checked by hand.
More from DeepSeek
All 22Other inference and efficiency papers
TopicAbout this paper
- Authors
- Chenggang Zhao, Chengqi Deng, Chong Ruan and 12 more
- arXiv
- 2505.09343 · PDF
- Venue
- International Symposium on Computer Architecture
- Citations
- 107, 11 influential · Semantic Scholar
- Upvotes
- 77 · Hugging Face
- Lab
- DeepSeek · on Companies
Changes
| What changed | |
|---|---|
| Sep 25, 2026 | Influential citationsfirst count: 11Sep 25, 2026 |
| Sep 25, 2026 | Citationsfirst count: 107Sep 25, 2026 |
| Sep 25, 2026 | New paperFound by the weekly scan, unverifiedSep 25, 2026 |
Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.