mamba-architecture

Linear-complexity state-space models achieve O(n) scaling versus Transformers' quadratic complexity, eliminating KV-cache overhead and enabling million-token contexts with faster inference speed and lower memory requirements.

Yes. Mamba provides LLM-compatible blocks (Mamba-1 and Mamba-2) with selective mechanisms and hardware-aware CUDA kernels, enabling efficient long-context language models without attention layers across PyTorch and HuggingFace workflows.

Deploy on GPUs with PyTorch, HuggingFace transformers, causal-conv1d, and mamba-ssm. Mamba uses CUDA-optimized kernels for hardware-aware execution, supporting streaming inference and configurable model blocks.

State-space selective mechanisms enable hardware-efficient streaming inference on sequences with hundreds of thousands of tokens, processing input-dependent parameters without KV-cache dependency for real-time outputs.

Mamba-1 uses d_state=16 for efficiency; Mamba-2 uses d_state=128 with multi-head structure for higher capacity. Both achieve linear complexity; choose based on context length, latency, and throughput requirements.

State-space blocks integrate into training workflows as drop-in replacements for attention layers. Instantiate with d_model, d_state, and d_conv parameters, then train end-to-end on PyTorch with standard optimization pipelines.