How do we choose between Transformer-based architectures and State Space Models like Mamba

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Transformers are still the safest bet for most production environments because the ecosystem is so mature. You have optimized kernels like FlashAttention-2 that mitigate some of those quadratic scaling issues. However, if your specific use case involves infinitely long sequences or real-time streaming data, Mamba’s linear scaling is a game-changer. I’ve seen teams reduce memory overhead by 5x using SSMs for long-document summarization. The catch is that the tooling isn't quite there yet, so expect to spend more time on custom CUDA kernels if you stray from the standard Attention path.

   Answered 2025-01-18 by Katherine Sullivan

Are you primarily worried about the training stability or the latency during real-time inference? I've found that while Mamba scales better, training it to the same "Common Sense" reasoning level as a GPT-style model requires significantly more data curation.

   Answered 2025-01-20 by Steven Higgins

• Steven, our main bottleneck is definitely inference latency. We are building a legal-tech tool that needs to ingest 100-page contracts in seconds. The quadratic cost of Transformers makes the per-request pricing almost impossible for our margins. We are willing to put in the extra data work if it means we can actually scale our API without going bankrupt on GPU cloud costs in the long run.

     Commented 2025-01-22 by David Miller

Hybrid models are emerging as a middle ground. Using Attention for local context and SSMs for long-range dependencies seems to be the sweet spot for many research teams right now.

   Answered 2025-01-24 by Maria Hernandez

• I agree with Maria. A hybrid approach allows you to keep the high-quality reasoning of Attention while offloading the heavy lifting of long sequences to a more efficient linear structure.

     Commented 2025-01-27 by Katherine Sullivan