Name: Towards AI
Legal Name: Towards AI, Inc.
Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world.
Phone Number: +1-650-246-9381
Email: pub@towardsai.net
228 Park Avenue South
New York,
NY
10003
United States
Website: https://towardsai.net/
Publisher: https://towardsai.net/#publisher
Diversity Policy: https://towardsai.net/about
Ethics Policy: https://towardsai.net/about
Masthead: https://towardsai.net/about
Name: Towards AI
Legal Name: Towards AI, Inc.
Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication.
Founders:
Roberto Iriondo,
Website,
Job Title: Co-founder and Advisor
Works for: Towards AI, Inc.
Follow Roberto:
X,
LinkedIn,
GitHub,
Google Scholar,
Towards AI Profile,
Medium,
ML@CMU,
FreeCodeCamp,
Crunchbase,
Bloomberg,
Roberto Iriondo, Generative AI Lab,
Generative AI Lab
VeloxTrend
Ultrarix Capital Partners
Denis Piffaretti,
Job Title: Co-founder
Works for: Towards AI, Inc.
Louie Peters,
Job Title: Co-founder
Works for: Towards AI, Inc.
Louis-François Bouchard,
Job Title: Co-founder
Works for: Towards AI, Inc.
Cover:
Logo:
Areas Served: Worldwide
Alternate Name: Towards AI, Inc.
Alternate Name: Towards AI Co.
Alternate Name: towards ai
Alternate Name: towardsai
Alternate Name: towards.ai
Alternate Name: tai
Alternate Name: toward ai
Alternate Name: toward.ai
Alternate Name: Towards AI, Inc.
Alternate Name: towardsai.net
Alternate Name: pub.towardsai.net
5 stars – based on
497 reviews
Frequently Used, Contextual References
TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e
Resources
Last Updated on April 23, 2026 by Editorial Team
Originally published on Towards AI.
DeepSeek-R1: 671 billion parameters. 37 billion active per token.

DeepSeek-R1: 671 billion parameters. 37 billion active per token.The article discusses various machine learning models, focusing on their parameter count and operational efficiencies. It delves into the architecture of the Mixture of Experts (MoE), detailing how different models utilize parameters per token and how routing affects performance, emphasizing the benefits of utilizing multiple experts for token processing to improve training stability and efficiency. Additionally, it explores specific implementations, such as the DeepSeek model, and compares it with existing architectures to elucidate advantages in computation and memory usage.
Read the full blog for free on Medium.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.