Tech · Hugging Face
Mellum2 is competitive with similarly sized open models while delivering more than 2x faster inference
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The MoE architecture keeps total model capacity high while activating only a subset of parameters for each token.
Key facts
- Mellum2 is competitive with similarly sized open models while delivering more than 2x faster inference, making it suitable for high-throughput production workloads
- If you are building AI systems for software engineering, inside an IDE, in a RAG pipeline, as part of an agent workflow, or on private infrastructure, Mellum2 is ready to try
- Modern AI systems increasingly rely on multiple model calls: routing, retrieval, summarization, planning, validation, and tool use
- As AI systems mature, the most effective architectures are becoming less monolithic
Summary
Mellum2 is a 12B-parameter Mixture-of-Experts model trained from scratch on natural language and code. The model activates only 2.5B parameters per token, making it efficient for high-throughput, low-latency inference. It is released under the Apache 2.0 license. Compared with similar-sized models, Mellum2 delivers competitive benchmark performance while achieving more than 2x faster inference. For architecture details, training setup, benchmarks, and evaluation methodology, read the full technical report:.