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Trained a 12M transformer on an ML framework we built from scratch
Custom CUDA kernels and Rust backend with a TypeScript API built by students in four months.
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caliandbust
221mo ago
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Mamba SSM and Mamba-3 SISO in Rust with optional CUDA GPU acceleration. Inference and training (BPTT through SSM state, AdamW), CPU + GPU paths, custom CUDA kernels, CUDA Graph capture, f32 / bf16 / f16. Batch-invariant bf16 inference — per-row output is bit-identical across batch sizes.
10 starsRust
Mamba SSM in Rust – training and inference with custom CUDA kernels
by silvermpx·Mar 23, 2026·1 point·0 comments
AI Analysis
●●SolidWizardryNiche Gem
Custom CUDA kernels for SSM recurrence with zero framework dependencies.
Strengths
- •Full BPTT through recurrent SSM state enables actual training, not just inference.
- •Zero-allocation single-step inference hits ~200μs on CPU without GPU.
- •Standalone design means no PyTorch, Burn, or Candle dependency chain.
Weaknesses
- •Mamba implementations already exist in multiple languages; Rust isn't unique.
- •No benchmark comparisons against official Mamba or other ports.
Category
Target Audience
ML engineers wanting Rust-based SSM implementations
Similar To
mamba-minimal · Candle · Burn
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