silvermpx

silvermpx / mamba-rs

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Mamba SSM in Rust with CUDA. Training + inference, forward + backward (BPTT), burn-in, custom CUDA kernels.

11
0
100% credibility
Found Mar 25, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A standalone Rust library for running and training Mamba models, efficient sequence processors that rival transformers with linear-time scaling.

How It Works

1
🔍 Discover speedy sequence smarts

You hear about a super-fast way to make computers understand long patterns like stories or music.

2
📦 Add the magic brain

You bring this clever tool into your creation space with a simple grab.

3
⚙️ Shape your thinker

You pick the right size brain and fill it with learned patterns to make it smart.

4
🚀 Start chatting sequences

You feed it bits of info one by one, and it remembers and responds lightning-quick.

5
⚡ Teach it more if needed

If you want it sharper, show it examples and let it learn from mistakes.

🎉 Your app dreams big

Now your project handles endless streams of data smoothly, feeling alive and powerful.

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Star Growth

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AI-Generated Review

What is mamba-rs?

mamba-rs is a Rust crate implementing the Mamba SSM from the mamba ssm paper, delivering fast inference and full training support including forward/backward passes with BPTT and burn-in. It runs on CPU or CUDA GPUs via custom kernels, staying completely standalone without PyTorch, Burn, or Candle dependencies. Developers get zero-allocation recurrent steps for real-time use and batched training paths matching official mamba_ssm github behavior.

Why is it gaining traction?

Stands out with framework-free design—no autograd overhead, just hand-derived gradients for lean training. Hits sub-400μs CPU inference latency and TF32 Tensor Core speedups on H100/GH200, beating Python mamba ssm pytorch baselines in dispatch costs. Custom CUDA via NVRTC compiles kernels at runtime, sidestepping conda/mamba github install hassles.

Who should use this?

Rust ML devs training compact Mamba models for edge inference or games needing linear-time sequences. Teams ditching mamba github pytorch for native Rust/CUDA pipelines, especially on Windows or without conda/mamba github actions. Suited for mamba ssm tutorial prototypes scaling to production without Python deps.

Verdict

Grab it for mamba-rs experiments—strong benchmarks and quickstart make prototyping painless, despite low 11 stars and 1.0% credibility signaling early maturity. Solid for toys or research; wait for more adoption before betting the farm.

(198 words)

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