ZAYA1-8B: An 8B Moe Model with 760M Active Params Matching DeepSeek-R1 on Math
firethering.com
May 7, 2026
6 min read
๐ฅ๐ฅ๐ฅ๐ฅ๐ฅ
52/100
Summary
ZAYA1-8B matches DeepSeek-R1 on math benchmarks and remains competitive with Claude Sonnet 4.5 on reasoning tasks. The model, trained entirely on AMD hardware, operates with less than 1 billion active parameters while closing in on Gemini 2.5 Pro in coding performance.
Key Takeaways
- ZAYA1-8B matches DeepSeek-R1 on math benchmarks and remains competitive with Claude Sonnet 4.5 on reasoning, despite having less than 1 billion active parameters.
- ZAYA1-8B was trained entirely on AMD hardware, specifically using AMD Instinct MI300X GPUs, demonstrating that AMD can produce competitive AI models without relying on NVIDIA infrastructure.
- The model employs a mixture of experts approach, activating only 760 million parameters during inference while leveraging a total of 8.4 billion parameters, achieving high performance at a lower active parameter count.
- ZAYA1-8B outperforms similar models like Qwen3-4B and Gemma 4 E4B across multiple math benchmarks, with significant score margins in tests such as AIME 2026 and HMMT.
Coverage & discussions
Hacker News ยท firethering.com
Community discussions
Hacker News discussion โ
Historical count: 5 ยท 5 saved in sample ยท Historical sample
Analysis ready
Community Sentiment
Positives
- The model's ability to perform math and coding tasks impressively suggests a promising future for local LLMs as replacements for larger models like Claude and OpenAI.
- The discussion around small models indicates a shift towards more efficient AI solutions that can operate effectively on commodity hardware without internet reliance.
- The successful coding output from a model with only 760M active parameters demonstrates the potential for cost-effective AI tools that can compete with larger models.
Concerns
- Concerns about the model's agentic capabilities highlight the ongoing challenges in achieving true reasoning and intelligence in smaller models.
- The reliance on larger models for commercially valuable projects raises questions about the practical limitations of smaller local LLMs.
![[AINews] Why OpenAI Should Build Slack](https://substackcdn.com/image/fetch/$s_!XQAE!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89ee056a-0ea2-4473-8e1c-9b21f034c717_1474x2116.png)
