FusionLLM: An Efficient Large Language Model Architecture Fusing MLA, Mamba-2, and MoE

FusionLLM is an open-source hybrid architecture large language model that integrates multi-head latent attention (MLA), gated delta network (Mamba-2), and mixture of experts (MoE) technologies. It aims to address the bottlenecks of low efficiency in long-sequence processing and high inference cost of the Transformer architecture, achieving efficient pre-training and inference and being production-ready. The project is maintained by atandra2000, open-sourced on GitHub, and released on June 9, 2026.

The current large language model field faces the bottlenecks of low long-sequence processing efficiency and high inference cost of the Transformer architecture. Technical routes such as DeepSeek's MLA, Mamba series state space models, and various MoE variants are all exploring next-generation architectures. FusionLLM's unique feature lies in fusing multiple cutting-edge technologies instead of betting on a single route, trying to combine the strengths of various approaches.

The three core technologies include: 1. Multi-head Latent Attention (MLA): Reduces KV cache memory usage through low-rank compression and handles short-range dependencies; 2. Gated Delta Network (Mamba-2): A state space model with linear complexity that captures long-range dependencies and optimizes hardware adaptation; 3. Mixture of Experts (MoE): Dynamically routes and activates some experts to achieve parameter expansion and load balancing. The fusion strategy adopts layered mixing (MLA in shallow layers, GDN in deep layers, MoE throughout), task-adaptive routing, and a unified training objective.

FusionLLM has production-ready features: In terms of inference efficiency, it supports KV cache reuse, operator fusion, and tensor/pipeline parallelism; In terms of training stability, it has targeted initialization strategies, load balancing processing, and support for large-scale distributed training; In terms of scalability, it uses a modular design and supports smooth expansion from 1B to tens of B parameters.

Potential application scenarios include long document processing (legal contracts, academic papers), real-time dialogue systems, and edge deployment, which are suitable for scenarios requiring efficient long-sequence processing or low-latency inference.

The project faces challenges including complex hyperparameter tuning for the hybrid architecture, low interpretability of interactions between different mechanisms, and compatibility issues with the existing Transformer ecosystem (such as LoRA fine-tuning and quantization).

FusionLLM represents the direction of large model architecture fusion. Its open-source release provides a hybrid architecture benchmark for the research community and demonstrates the path of transforming cutting-edge technologies into deployable systems for the industry. Its idea of balancing efficiency and capability is worthy of attention and is an important reference in the field of architectural innovation.